From 2e954d04f43d40f51ccdef24c97c535b4fb48b77 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Tue, 12 Nov 2019 16:32:06 -0300
Subject: [PATCH 01/14] Created using Colaboratory

---
 notebooks/3_regression.ipynb | 3981 ++++++++++++++++++++++++++++++++++
 1 file changed, 3981 insertions(+)
 create mode 100644 notebooks/3_regression.ipynb

diff --git a/notebooks/3_regression.ipynb b/notebooks/3_regression.ipynb
new file mode 100644
index 0000000..8921f6a
--- /dev/null
+++ b/notebooks/3_regression.ipynb
@@ -0,0 +1,3981 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "3_regression.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "TsV0SiuwpZ7i",
+        "7cfguFoQp2_-",
+        "hDpjk2hPqBar",
+        "fB8cnF96qcQM",
+        "MyrmVTcFtw9L",
+        "Ood5PUZCtxsa",
+        "VUZAwJjMvh0r",
+        "KDcimzDmxxIo"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/3_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "#**Semana 3 - Modelos de Regressão**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TsV0SiuwpZ7i",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs e funções:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regressão\n",
+        "def plot_results_linear(X, y, regressor, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
+        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()    \n",
+        "    \n",
+        "# Função que gera o gráfico dos resultados de arvores\n",
+        "def plot_results_reg(X, y, regressor, title):     \n",
+        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
+        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "     "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QaGBc_KwpeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos **dados**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/r4phael/ml-course/master/data/pricing_houses.csv')\n",
+        "\n",
+        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "cc0eccc5-6bb5-4ead-a7b9-7596700a9280",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "df.describe()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>11633.933333</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>504.683333</td>\n",
+              "      <td>5.783333</td>\n",
+              "      <td>1972.116667</td>\n",
+              "      <td>197536.150000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8099.879028</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>222.427554</td>\n",
+              "      <td>1.180228</td>\n",
+              "      <td>30.522618</td>\n",
+              "      <td>74910.507145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>8068.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.750000</td>\n",
+              "      <td>142250.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10510.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>542.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.500000</td>\n",
+              "      <td>188750.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12010.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>607.250000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>53227.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
+              "count     60.000000      60.0  ...    60.000000      60.000000\n",
+              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
+              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
+              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
+              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
+              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
+              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
+              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "7ef0eb04-db39-4e20-bcd3-a2c266e5e15a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>789</th>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1039</th>\n",
+              "      <td>1477</td>\n",
+              "      <td>0</td>\n",
+              "      <td>286</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1970</td>\n",
+              "      <td>RM</td>\n",
+              "      <td>80000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>868</th>\n",
+              "      <td>14762</td>\n",
+              "      <td>0</td>\n",
+              "      <td>672</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1948</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>169000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>782</th>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>10395</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>148000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "789     12205         0         444            8       1966       RL     187500\n",
+              "1039     1477         0         286            4       1970       RM      80000\n",
+              "868     14762         0         672            6       1948       RL     169000\n",
+              "782     16285         0         605            5       2001       RL     187100\n",
+              "611     10395         0         564            6       1978       RL     148000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "ee2fbfca-21aa-4e0d-8410-502c6c747df6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>789</th>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1039</th>\n",
+              "      <td>1477</td>\n",
+              "      <td>0</td>\n",
+              "      <td>286</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1970</td>\n",
+              "      <td>RM</td>\n",
+              "      <td>80000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>868</th>\n",
+              "      <td>14762</td>\n",
+              "      <td>0</td>\n",
+              "      <td>672</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1948</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>169000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>782</th>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>10395</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>148000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "789     12205         0         444            8       1966       RL     187500\n",
+              "1039     1477         0         286            4       1970       RM      80000\n",
+              "868     14762         0         672            6       1948       RL     169000\n",
+              "782     16285         0         605            5       2001       RL     187100\n",
+              "611     10395         0         564            6       1978       RL     148000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "78f424a9-14e0-42ae-c0b1-5a9d20472a25",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[12205],\n",
+              "       [ 1477],\n",
+              "       [14762],\n",
+              "       [16285],\n",
+              "       [10395]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "fe5cbf44-fe44-4b28-9baf-a50ecf037b33",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[187500],\n",
+              "       [ 80000],\n",
+              "       [169000],\n",
+              "       [187100],\n",
+              "       [148000]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yzJ08rJ4J3CS",
+        "colab_type": "text"
+      },
+      "source": [
+        " Criando os subconjuntos de treinamento e testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tVz1mTUOJ4Tb",
+        "colab_type": "code",
+        "outputId": "4f5b4396-f9c4-4d11-c71d-7227ea74ee4c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        }
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
+        "\n",
+        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
+        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
+        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Tamanho do Dataset. 60\n",
+            "Tamanho do Conjunto de Treinamento. 48\n",
+            "Tamanho do Conjunto de Testes. 12\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#X_train = feature_scaling(X_train)\n",
+        "#X_test = feature_scaling(X_test)\n",
+        "\n",
+        "#X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7cfguFoQp2_-",
+        "colab_type": "text"
+      },
+      "source": [
+        "###  Modelo - Regressão Linear"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jGXuG35WJ_Tj",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xo9Q5lxsKAVB",
+        "colab_type": "code",
+        "outputId": "d60bc5b4-d05e-4e4e-da6b-e51867d2076c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FUYWJqnaKBvh",
+        "colab_type": "text"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SFvsig4SKCiC",
+        "colab_type": "code",
+        "outputId": "a8b625a1-50b2-4b45-f4cf-c02a757b93ea",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[207117.08544721],\n",
+              "       [183686.58545274],\n",
+              "       [169299.32577351],\n",
+              "       [179936.94922447],\n",
+              "       [188476.03676953],\n",
+              "       [191960.99279345],\n",
+              "       [202201.59593788],\n",
+              "       [465634.02218018],\n",
+              "       [206688.55559255],\n",
+              "       [176212.52063471],\n",
+              "       [195653.91183507],\n",
+              "       [185759.91372014]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kRjvN45RKFPq",
+        "colab_type": "text"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r²"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "I3_jQ8N5KH-6",
+        "colab_type": "code",
+        "outputId": "b6945795-2198-4faf-9dab-41d33ef3fa65",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-0.9506477517227611"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rTahZRcqKS2b",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2EAqhxovKT9a",
+        "colab_type": "code",
+        "outputId": "64515476-b062-45cf-94f4-4c2933868406",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_linear(X_train, y_train, regressor, 'Regressão Linear (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Lc86VsVWrQn+p228Pa9eG2J13hqRy/PHFK5cnF+ecK0MffQT77AO9e8PChSF27bUhqYwZ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\n55zLO08uzjnn8u7/AwrycyiLfw2dAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hwRwnq82KU2w",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uXKEZrWQKV9R",
+        "colab_type": "code",
+        "outputId": "6ac6a682-61d0-4171-852e-dfc66cf6fa0e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_linear(X_test, y_test, regressor, 'Regressão Linear (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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/+UtKKrvsUrtymXUlbSaX3Jz4E5JuJlWkHwCcSmOHyrZsSXro5SxSIpgaEbcC\npwDflNRAqh+5Mm9/JbBZjn8zn4dc13MjaQTMO4Cv5tttK4ETSB085wE3FuqFWjuHWYctXQqDBsHg\nwY2xm29OSWW//WpXLrOuSOmLfgsrpF8CBwF/IbXE+kMelbJHqquri/r6+loXw7qg119PyaP453HZ\nZXDccbUrk1lXIWlGRNQ1j7fVWuwO4LiIeKV6xTLrulauhI9/HCZPboyddRaceWbNimTWbbSaXCLi\n2s4siFlXEQFf/SpcemljbPz4dLUi1a5cZt1JOeO5mPUa55wDZ5zRuHzYYfDb36bHt5hZ+fwvYwZc\ncQV86UuNyyNHwt13v/2pO2ZWnjUmF0n9gZOBIRHxJUnDge1zyy+zbu2WW+CIwsOB3vUumDsXNt20\ndmUy6wnK6efyC9JDKEtdxRYC51StRGad4J57Uv1JMbE880waGdKJxWztlZNcto2IHwNvAUTEa4Cr\nNa1bmjMnJZUPfKAxNnduqsTfeuvW9zOz9iknuazIPexL47lsS7qSMes2nnkmJZWdd26M/e1vKakU\ne9qbWWWUk1zOJPV52VrSROBO4NtVLZVZhbzwQnp45NChjbHJk1NS2Wef2pXLrKdbY4V+REyV9A9g\nFOl22IkR8a+ql8xsLbz6akoeswojD115JXzhC7Urk1lv0mpykTSyWWhR/jlE0pA8iJhZl/LWW/DR\nj8IddzTGzj03jVdvZp2nrSuXn+Sf6wF1wEOkK5ddgHoaW4+Z1VwEfPGLcNVVjbGvfS0NMexe9Wad\nr63xXD6Ux3JZBIzMg2rtDuyGx0exLuS7301j0pcSy3/9V3ou2MUXO7GY1Uo5PfS3j4jZpYWImCNp\nhyqWyawsP/tZegZYyahRMG0arLde7cpkZkk5rcVmSbpC0v55uhyYtca9zKrkppvSFUkpsQwZksZa\nufdeJxazdpk4MQ2b+o53pJ8TJ1bs0OVcuRwDfBk4MS//lTQipVmn+vOf4UMfalxeZx14+mnYqpyh\n68ysqYkT0+O+X3stLT/9dFoGGDt2rQ/f6mBhvY0HC+u6Zs6E3XZrGnvkEdh++9qUx6xHGDYsJZTm\nhg6F+fPLPkxHBgszq6mnnoJ3v7tp7L77YM89a1Mesx7lmWfaF2+ncupcOkTS1pKmSXpY0lxJJ+b4\nWZIWSpqZp0MK+5wmqUHSo5IOLsTH5FiDpFML8W0k3ZfjN0haN8f75eWGvH5YtV6nVd7ixbDxxk0T\ny+23p+bGTixmFTJkSPvi7VR2cpG0oaQN23HslcDJETGC1Lv/q5JKT3G6ICJ2zdNt+fgjgKOAHYEx\nwM8k9ZHUB7gE+AgwAvh04Rw0rMIAABajSURBVDg/ysfaDlgKHJvjxwJLc/yCvJ11ccuXww47wBZb\nwCt5cO3rrktJZcyY2pbNrMf5wQ/ePmBR//4pXgFrTC6Sdpb0IDAXeFjSDEk7rWm/iFhU6sUfEa8A\n84BBbexyODApIt6MiKeABmDPPDVExJMRsQKYBBwuScABwE15/2uAIwrHuibP3wQcmLe3LmjFCjjw\nQNhoo1SXAnD++SmpfPaztS2bWY81dixMmJDqWKT0c8KEilTmQ3lXLj8HvhkRQyNiCGngsAntOUm+\nLbUbcF8OnSBplqSrJG2SY4OAZwu7Lcix1uKbAS9FxMpm8SbHyuuX5e2bl2u8pHpJ9UuWLGnPS7IK\nWL0aPvc56NcP7rorxU4+OcVPPrm2ZTPrFcaOTZX3q1ennxVKLFBectkgIqaVFiLiz8AG5Z4g30r7\nDXBSRLxMasa8LbArqff/T9rYvaoiYkJ+8kDdwIEDa1WMXicCTjkF+vSB669PsU9/GlatSlcsvsY0\n6/7KaS32pKQzgOvy8meBJ8s5uKS+pMQyMSJuBoiI5wvrLwdKwyUvBIrDNQ2m8TEzLcVfAAZIWidf\nnRS3Lx1rgaR1gHfm7a3GLroITjqpcXm//eCPf0xXL2bWc5Rz5fIFYCBwMylRbJ5jbcp1HFcC8yLi\nfwvxLQubfQyYk+cnA0flll7bAMOB+4EHgOG5Zdi6pEr/yZE66EwDjsz7jwNuKRxrXJ4/Ergr3KGn\npn71q3RFUkos220Hy5bBX/7ixGLWE5Vz5bKcNIZLaSRKlbnfvsDngNmSZubYd0itvXYljWw5HzgO\nICLmSroReJjU0uyrEbEqn/MEYArQB7gqIubm450CTJJ0DvAgKZmRf14nqQF4kZSQrAamToXRoxuX\n+/eHJ59MLcLMrOdaYw99SfcCo3OLLyRtDNwOHA30jYhHql7KTuAe+pU1YwbUNeuz29AA225bm/KY\nWXW01kO/nNti65cSC0CulB8GfBz4fcVKaD3C44+n21/FxDJjRqrEd2Ix6z3KSS6vSnpfaUHSbsCz\nEfFjGus4rJd77rn0ROL3vKcxNnVqSiojm49pamY9Xjl1JycBv5H0T9JIlO8i12FExLeqWDbrBpYt\nSw+VfOqpxtikSfCpT9WuTGZWe2tMLhHxQB4crPQM2kcj4q3qFsu6ujffhIMOgnvuaYxddBF8/eu1\nK5OZdR3lPP6lP6lV1okRMQcYJumwqpfM1k6VBgFatSpdlay3XmNiOe20dPvLicXMSsqpc/kFsALY\nOy8vBM6pWols7ZUGAXr66fSpXxoEaC0STAR84xtpgK4bb0yxceNSsjn33AqV28x6jHKSy7a58v4t\ngIh4jVT3Yl3V6ac3ji5X8tprKd4BP/5xugC68MK0/OEPp9tiV1+d4mZmzZVTob9C0vqkTo9I2hZ4\ns6qlsrVToUGArr02XZ2U7LgjTJ8OG7Zn4AUz65XKSS5nAncAW0uaSOp5//lqFsrW0pAhLQ9fWuYg\nQLfdBoce2rg8YAA89hj42Z5mVq42b2rkR708AvwXKaH8CqjLT0a2rqqDgwBNn546QBYTy1NPwdKl\nTixm1j5tXrlEREi6LSJ2Bv7QSWWytVUak+H009OtsCFDUmJpZayGRx5JI0AWPfQQ7LJLlctpZj1W\nObfF/iFpj4h4oOqlscoZO3aNA/8sXAiDBzeN/fnP8MEPVq9YZtY7lNPWZy9guqQn8uiRsyXNqnbB\nrHqWLoVBg5omlptuSs2NnVjMrBLKuXI5uOqlsE7x+utpcK7iw58vvRSOP752ZTKznqnV5CJpPeB4\nYDtgNnBlYbx660ZWroQjj4RbCo8ZPfNMOOusmhXJzHq4tq5criF1nLwb+AgwAjixMwpllRGR+qlc\nd11jbPx4uOwyj1NvZtXVVnIZkVuJIelK0pDD1k2MHp0eeV9y6KHwu9+lx7eYmVVbWxX6/37ycUdu\nh0naWtI0SQ9LmivpxBzfVNJUSY/nn5vkuCRdLKkhNxwYWTjWuLz945LGFeK75wYGDXlftXWO3uAr\nX0lXJcXE8uqrcOutTixm1nnaSi7vk/Rynl4BdinNS3q5jGOvBE6OiBHAKOCrkkYApwJ3RsRw4M68\nDOnW2/A8jQcuhZQoSE8J2AvYEzizkCwuBb5U2G9Mjrd2js5TpacSt+a881JSufTSxtiCBenWWPP+\nlGZm1dZqcomIPhGxcZ42ioh1CvMbr+nAEbEoIv6R518B5gGDgMNJ9Tnkn0fk+cOBayOZDgyQtCWp\ntdrUiHgxIpYCU4Exed3GETE9IgK4ttmxWjpH56jCU4lb88tfpqTy7W83xmbPTqcdNKjipzMzK0un\nPNNW0jBgN+A+YIuIWJRXPQdskecHAc8WdluQY23FF7QQp41zdI4KP5W4JdOmpaRS7Cc5bVpKKjvt\nVLHTmJl1SNWTi6QNgd8AJ0VEk9tp+Yojqnn+ts4habykekn1S5YsqdxJK/RU4pbMnp2SygEHNMYm\nTUpJZf/91/rwZmYVUdXkIqkvKbFMjIibc/j5fEuL/HNxji8Eti7sPjjH2ooPbiHe1jmaiIgJEVEX\nEXUDK/lkxtaePlzmU4lb8tBDKakUn/d1/vkpqXi8ejPraqqWXHLLrSuBeRHxv4VVk4FSi69xwC2F\n+NG51dgoYFm+tTUFGC1pk1yRPxqYkte9LGlUPtfRzY7V0jk6RzlPJS6zwv+f/0xJZdddG2MnnJCS\nysknV7zkZmaVERFVmYD3k25HzQJm5ukQYDNSC67HgT8Bm+btBVwCPEF6IkBd4VhfABrydEwhXgfM\nyfv8H6Acb/EcbU277757VNT110cMHRohpZ/XX990Xf/+ESlHpKl//ybbLF/edHVpqsj5zcwqBKiP\nFj5TSx/GvV5dXV3UFx+6VU3DhrU8mNfQoax6Yn6L/VFWr25Hr/pSa7Vio4L+/WHChDU+KdnMrD0k\nzYiIuuZxj4BeC61U7OvptyeWN95I1yztelxLJ7RWMzNri5NLLTSr2BeBmjVoW7w4JZV+/Tpw/Cq2\nVjMzK4eTSy3kCv9P88u3JZV581JSWavGa1VorWZm1h5OLjVw1uNj0WuvMolP/zt213f+RAS8970V\nOEE5rdXMzKrIjzLsRNdemx6BX3TLLfDRjwIcVLkTlSrtTz893QobMiQlFlfmm1kncXLpBHfdBQce\n2DT205+m/ipVM3ask4mZ1YyTSxU98wwMHdo0duKJcOGFtSmPmVlncZ1LFSxfDjvs0DSxHHxwqqhv\nM7F08mP6zcyqxcmlglasgIMOgo02gkceSbEvfSkllTvuWMPOnfiYfjOzanNyqYDVq+Hoo1OflDvv\nTLGTT07xCRPKPIg7PppZD+I6l7U0cybstlvj8mc+A9ddl+5stYs7PppZD+Irl7X04IPp5/77p0e1\nTJzYgcQC7vhoZj2Kk8taOuaYVEUybVoHH9VS4o6PZtaDOLl0FWPHpgqaoUPTUyqHDvVTjM2s23Kd\nS1fijo9m1kP4ysXMzCrOyaXW3HHSzHog3xarpeYjRpY6ToJvj5lZt1a1KxdJV0laLGlOIXaWpIWS\nZubpkMK60yQ1SHpU0sGF+Jgca5B0aiG+jaT7cvwGSevmeL+83JDXD6vWa1xr7jhpZj1UNW+LXQ2M\naSF+QUTsmqfbACSNAI4Cdsz7/ExSH0l9gEuAjwAjgE/nbQF+lI+1HbAUODbHjwWW5vgFebuuqa2O\nk75dZmbdWNWSS0T8FXixzM0PByZFxJsR8RTQAOyZp4aIeDIiVgCTgMMlCTgAuCnvfw1wROFY1+T5\nm4AD8/ZdT2sdJDfd1M8ZM7NurRYV+idImpVvm22SY4OAZwvbLMix1uKbAS9FxMpm8SbHyuuX5e27\nntY6ToJvl5lZt9bZyeVSYFtgV2AR8JNOPn8TksZLqpdUv2TJks4vQGsdJ19s5YLPzxkzs26iU5NL\nRDwfEasiYjVwOem2F8BCYOvCpoNzrLX4C8AASes0izc5Vl7/zrx9S+WZEBF1EVE3cODAtX15HTN2\nLMyfnx6hPH9+WvZzxsysm+vU5CJpy8Lix4BSS7LJwFG5pdc2wHDgfuABYHhuGbYuqdJ/ckQEMA04\nMu8/DrilcKzSSPVHAnfl7bsPP2fMzLq5qvVzkfQrYH9gc0kLgDOB/SXtCgQwHzgOICLmSroReBhY\nCXw1Ilbl45wATAH6AFdFxNx8ilOASZLOAR4ErszxK4HrJDWQGhQcVa3XWDWlPi6nn55uhQ0ZkhKL\n+76YWTeh7valvlrq6uqivr6+1sXomIkTnYjMrCYkzYiIuuZx99Dv7tzL38y6ID9brLtzL38z64Kc\nXLo7D49sZl2Qk0t352bLZtYFObl0d262bGZdkJNLd+fhkc2sC3JrsZ7AwyObWRfjKxczM6s4Jxcz\nM6s4J5e14QG9zMxa5DqXjnLPeDOzVvnKpaPcM97MrFVOLh3lnvFmZq1ycuko94w3M2uVk0tHuWe8\nmVmrnFw6yj3jzcxa5dZia8M9483MWuQrFzMzq7iqJRdJV0laLGlOIbappKmSHs8/N8lxSbpYUoOk\nWZJGFvYZl7d/XNK4Qnx3SbPzPhdLUlvnMDOzzlPNK5ergTHNYqcCd0bEcODOvAzwEWB4nsYDl0JK\nFMCZwF7AnsCZhWRxKfClwn5j1nCO2nJvfjPrRaqWXCLir8CLzcKHA9fk+WuAIwrxayOZDgyQtCVw\nMDA1Il6MiKXAVGBMXrdxREyPiACubXasls5RO6Xe/E8/DRGNvfmdYMysh+rsOpctImJRnn8O2CLP\nDwKeLWy3IMfaii9oId7WOWrHvfnNrJepWYV+vuKIWp5D0nhJ9ZLqlyxZUr2CuDe/mfUynZ1cns+3\ntMg/F+f4QmDrwnaDc6yt+OAW4m2d420iYkJE1EVE3cCBAzv8otbIvfnNrJfp7OQyGSi1+BoH3FKI\nH51bjY0CluVbW1OA0ZI2yRX5o4Eped3LkkblVmJHNztWS+eoHffmN7NepppNkX8F3AtsL2mBpGOB\nHwIflvQ4cFBeBrgNeBJoAC4HvgIQES8CZwMP5On7OUbe5oq8zxPA7Tne2jlqx735zayXUaqWsLq6\nuqivr691MczMuhVJMyKirnncPfTNzKzinFzMzKzinFzMzKzinFzMzKzinFzMzKzi3Fosk7QEeLrW\n5WjD5sC/al2IGurtrx/8HoDfA+h678HQiHhbL3Qnl25CUn1Lzf16i97++sHvAfg9gO7zHvi2mJmZ\nVZyTi5mZVZyTS/cxodYFqLHe/vrB7wH4PYBu8h64zsXMzCrOVy5mZlZxTi5mZlZxTi6dSNJVkhZL\nmlOIbSppqqTH889NclySLpbUIGmWpJGFfcbl7R+XNK4Q313S7LzPxXmsmy5F0taSpkl6WNJcSSfm\neK94HyStJ+l+SQ/l1/+9HN9G0n25zDdIWjfH++Xlhrx+WOFYp+X4o5IOLsTH5FiDpFM7+zWWS1If\nSQ9KujUv96r3QNL8/Hc6U1J9jvWc/4OI8NRJE7AfMBKYU4j9GDg1z58K/CjPH0Iao0bAKOC+HN+U\nNPbNpsAmeX6TvO7+vK3yvh+p9Wtu4T3YEhiZ5zcCHgNG9Jb3IZdpwzzfF7gvl/VG4Kgcvwz4cp7/\nCnBZnj8KuCHPjwAeAvoB25DGNOqTpyeAdwPr5m1G1Pp1t/JefBP4JXBrXu5V7wEwH9i8WazH/B/4\nyqUTRcRfgRebhQ8Hrsnz1wBHFOLXRjIdGKA0bPPBwNSIeDEilgJTgTF53cYRMT3SX9a1hWN1GRGx\nKCL+kedfAeYBg+gl70N+HcvzYt88BXAAcFOON3/9pfflJuDA/A30cGBSRLwZEU+RBs3bM08NEfFk\nRKwAJuVtuxRJg4FDSQP+kV9Tr3oPWtFj/g+cXGpvi0jDNgM8B2yR5wcBzxa2W5BjbcUXtBDvsvLt\njd1I3957zfuQbwfNBBaTPgyeAF6KiJV5k2KZ//068/plwGa0/33pai4Evg2szsub0fvegwD+KGmG\npPE51mP+D9bpzJNZ2yIiJPWKtuGSNgR+A5wUES8Xbwf39PchIlYBu0oaAPwWeG+Ni9SpJB0GLI6I\nGZL2r3V5auj9EbFQ0n8AUyU9UlzZ3f8PfOVSe8/nS1jyz8U5vhDYurDd4BxrKz64hXiXI6kvKbFM\njIibc7jXvQ8R8RIwDdibdJuj9GWvWOZ/v868/p3AC7T/felK9gU+Kmk+6ZbVAcBF9K73gIhYmH8u\nJn3J2JMe9H/g5FJ7k4FSC49xwC2F+NG5lcgoYFm+XJ4CjJa0SW5JMhqYkte9LGlUvh99dOFYXUYu\n25XAvIj438KqXvE+SBqYr1iQtD7wYVK90zTgyLxZ89dfel+OBO7K99AnA0flllTbAMNJFbgPAMNz\ny6t1SRXgk6v/ysoXEadFxOCIGEYq310RMZZe9B5I2kDSRqV50t/vHHrS/0Fnth7o7RPwK2AR8Bbp\nHuixpHvHdwKPA38CNs3bCriEdD9+NlBXOM4XSJWXDcAxhXgd6Q/0CeD/yE9g6EoT8H7SveZZwMw8\nHdJb3gdgF+DB/PrnAN/N8XeTPhgbgF8D/XJ8vbzckNe/u3Cs0/NrfJRCS6D8fj6W151e69e8hvdj\nfxpbi/Wa9yC/1ofyNLdUxp70f+DHv5iZWcX5tpiZmVWck4uZmVWck4uZmVWck4uZmVWck4uZmVWc\nk4v1SpI2y0+jnSnpOUkLC8vrdnJZFpT6vlTgWPdI2rUa20saKWlMx0tnvYkf/2K9UkS8AOwKIOks\nYHlEnF/TQnV9I4GdgDtqXRDr+nzlYtaMpN/nhwnOlfTFHFtH0kuS/jfHp0jaS9JfJD0p6ZC83baS\n7lYap2SGpL1y/CBJd0q6WWmckWubnfakvM8sSe/J+2wuaXKO/V3STi2Utb+kX0uaJ+k3pA6HpXWf\nVRrPY46kc9vx+teXdE3e9x+S9stPE/guMDZf3R0paUNJVyuNT/OgpP9s73ttPZeTi9nbjYuI3YE9\ngG/mx2pAeqbV7RGxI7ACOAs4EPgE8P28zSLgwxGxGzAWuLhw3JHACaRxSHbIj/EoeT7vcwVpnBOA\ns0njduySz3V1C2U9AVgaETsA55CeMl16pP05wIdybF+lB0aW4+vAmxGxM/A54DpgVX6NEyNi14i4\niZRs7oiIPUnPB/uJpPVaO6j1Lk4uZm/3DUkPAfeSHvi3bY6/HhFT8/xs4M+RHgE/GxiW4/2AK5VG\nG51ESiQl0yPin5GeijyzsA9A6QGeMwrx95M+2ImIPwJb5edQFe0HXJ+3eZD0KBGAvUjP4PpXRLxF\nGpRrvzJf//sLx5wL/BPYroXtRgOnKw0fMI101TSkzHNYD+c6F7MCSQeRPoRHRcTrku6h8VbTisKm\nq4E3C/Ol/6WTSeNrfJY0ENjywj5vFuZX0fT/781W4l2ZgCMi4olaF8S6Hl+5mDX1TuDFnFh2JN0a\na+/+iyI9tG8c6QO4o+4m3VorJb2FEfFqs23+Cnwmb/M+YMccvw/4UG4Vtw7pycB/6cB5dyANTd0A\nvEIamrpkCvC10oKk3cp+ZdbjObmYNfUHoL+kh0l1Fve1c///A76Yb6ttQ9Orlfb6LrC3pFmk+o5j\nWjnfZpLmAWeQnrhMRCzIy38m3YKbHhF/aOU8U3Jz6AWSfgX8FFhf0mxgInB0pOGC7wLelyvvjwS+\nB2yQK/7nkuqFzAD8VGQzM6s8X7mYmVnFObmYmVnFObmYmVnFObmYmVnFObmYmVnFObmYmVnFObmY\nmVnF/X8AVoupKS8+XwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "hDpjk2hPqBar"
+      },
+      "source": [
+        "###  Modelo - Regressão Polynomial"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "5ZQOFczoqBat"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "07250634-6377-453d-e7e3-ef60148b086e",
+        "id": "TARpu6xZqBat",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        }
+      },
+      "source": [
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SAyttRhcqsz_",
+        "colab_type": "text"
+      },
+      "source": [
+        "Transformando as features na quarta ordem polinomial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OZyCBmL2qs-X",
+        "colab_type": "code",
+        "outputId": "509a28e7-a7bb-4290-cd72-3851df815bdc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "from sklearn.preprocessing import PolynomialFeatures\n",
+        "\n",
+        "poly_reg = PolynomialFeatures(degree = 4)\n",
+        "X_poly = poly_reg.fit_transform(X)\n",
+        "poly_reg.fit(X_poly, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PolynomialFeatures(degree=4, include_bias=True, interaction_only=False,\n",
+              "                   order='C')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92IzIbgrrDdG",
+        "colab_type": "text"
+      },
+      "source": [
+        "Treinando o modelo de regressão polynomial com o conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AiAG4XSPrDy9",
+        "colab_type": "code",
+        "outputId": "0c220401-e443-4669-e878-a9025f1455cb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        }
+      },
+      "source": [
+        "lin_reg_poly = LinearRegression()\n",
+        "lin_reg_poly.fit(X_poly, y)\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "5Xy5SEAuqBaz"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "c16cdc19-73c9-4409-c400-ce89852480ca",
+        "id": "ySpZ9RyWqBa0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y_pred = lin_reg_poly.predict(X_poly)\n",
+        "\n",
+        "y_pred[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[210977.15912637],\n",
+              "       [101264.45016613],\n",
+              "       [231210.99529702],\n",
+              "       [242927.64582841],\n",
+              "       [195940.94028164]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wUrWgrvU0uTq",
+        "colab_type": "text"
+      },
+      "source": [
+        "Avaliando o modelo de regressão linear com a métrica r2:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lETAHBqw0uC5",
+        "colab_type": "code",
+        "outputId": "f7368a63-cfcc-49ae-bdde-f1664f40d0f6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.14645648066355865"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "f8hyc24cqBa7"
+      },
+      "source": [
+        "Avaliando o modelo de regressão polynomial com a métrica r2:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "b77eb92e-2960-4140-de80-b473142322e3",
+        "id": "HDXwQ0ddqBa9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "lin_reg_poly.score(X_poly, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.2824360477057858"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "gaRZCCjKqBbC"
+      },
+      "source": [
+        "Visualizando os resultados da regressão linear:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "cd930c49-89d7-4214-ef23-73663647d20e",
+        "id": "GdApU1gYqBbD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_linear(X, y, regressor, 'Regressão Linear')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iM4T1CeU18eieezZ/Auztt0NSqakpTpmcKykFHi0kl+RyFjAfuDB+XouxnMVm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\n62UAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "LlX6EwYwqBbG"
+      },
+      "source": [
+        "Visualizando os resultados da regressão linear polinomial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "6e05d3f9-7557-4788-f409-316133d351ae",
+        "id": "5GKwud8fqBbG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_poly(X, y, lin_reg_poly, poly_reg, 'Regressão Polynomial')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ptp13hv/9zxJIXnCBKZZNm2w7nT32gKOPhrlzLZXLnDnmHe6Kxam1JForK1uw\nhJFDsaSSM4ER6bTLp+I2lzJkY5y1wJFh6tT4R2reXHX9+pq7/6ZNqs8+q3rYYaX3HzxYdfr0+Hpr\n1qjedptq9+5Wr3dv1bFjS9O3OE5tgepG6AMtgdOBKcDCdNvlS3FvsQTkyzjT5L//jVcsV11Vc/de\nsUL1xhtVt9tON0fJjxxZPvXK8uWqI0aotm9v9fbf3zIEFBfX3FgdpzIkUy5JDfoAItIMOBo4GTgA\neAlLYz9RQ2biukKNGfSdGkfVggxvvbVUNn267VWSbb7+2u774IOwerUFOg4daoGPjSIWzwULLPDx\nrrssy/KRR1rK+4MPNp8Hx8kWGzdC48ZVb19pg76IPAz8EngLGI/tlbKu6kNwnJpnw4Z4z6/mzS2C\nvTr/mSpCFSZONKP8Cy/YvU46yZRK377xdYuK4D//MeWzaROceCL87W+w557ZG59TP1E1h5HJk+PL\nkiX2Y6tnz8zeL5W32EvAOar6Y2Zv6Tg1w6JF0KFD6fk119hsIFv89BM89BDccgt88YXd+4or4Jxz\n4Gc/i6/76adw7bUWXd+4MfzhD/DXv5qnmONUF1XL3pBIkcTYcUc45BDL7rDddpkfQ1LloqpjM387\nx6kZXnjBkk7G+PLL7G2ANWeOxcvcey+sWGGzk7FjbRYSnTWpwttvm5J7+WWLY7nkEpvRdOyYnbE5\ndZ/iYvjqK1Men35a+rlyZWmd7be3nVILCqzstVf2t+NOJ3Gl4+QVRx8NEyaUnm/YkPllMFULZhw1\nCv77X7OLHH+8KYoDDoi3k5SUmIvxyJGW52ubbeDf/7ZMxq1aZXZcTt1mwwb4/PP42cjUqfGJzLt1\ng1/+slSR9OkDbdok6XD8ePNp/+47CzMYMQIGDszIWF25OHWGjRuhSZPS8wsvNCN5Jlm3Dh591JTK\nlCn2n/aSS+C88yz1fdnxPPKILX99/jn06GGBkmecYbYfx0nF2rUWCxVVJNOmmYKJ0bkz9OtXqkj6\n9rUA27QYPx4GDy7VTHPm2DlkRMFUqFxEpAVwMdBVVc8WkZ7ATqo6oYKmjlNjTJ8Ou+1Wej55sk39\nM8X335sn1113weLF5mk2erT9HywbG7pmDdx3H1x/vf0g3G03+3984onxHmKOE2P1avuxElUkn39u\nS14xfvaz8oqkrC2vUqTauykTs5dE/snRgu3dcikwPZy3AKak0a4LtiXy58AMYGiQXwHMx+JlpgAD\nIm0uA4qAr4AjIvL+QVYEDIvIewAfBfljQJMgbxrOi8L17hWN13eijJBn8S2XXRYfv7JuXeb6/ugj\n1VNOUW3UyL6Oo49WffXV+E/YvvsAACAASURBVASSMZYts9iZdu1sHAcdpDphQuK6Tv1l2TLV115T\n/c9/LPHoTjvZ31b0b7hdO9Ujj7SM2M8+qzp/fhYGUvamsSJSqW6oRlbkwvD5aUQ2NY12HYE+4XhL\nLJtyr6Bc/pqgfi9galAMPbBMAA1DmQlsBzQJdXqFNo8DJ4Xju4Bzw/EQ4K5wfBLwWEXjdeUSyJcM\nAmpKJDrMPfbITL8bNqg+8ojqfvtZv1tuqTp0qOo33ySuP2+e6sUXq7ZsafV//WvVd97JzFic/Gbh\nQtUXX7TA2N/+VrVHj/Lv8tatVX/1K/uR9NRTqnPm1NAPkgxlKq+OcnkfaE7pfi7bAx9X1C5BP88C\nv0qhXC4DLoucvwzsH8rLZethGZqXAI2CfHO9WNtw3CjUk1Tjc+USqOWp8WOUTeNy773V73PxYnsJ\ndOpkfe6wg+01v3Jl4vpffaV61lmWFr9hQ0uRP3Vq9cfh5B8lJapz59os4/LLbYYb+zuKlq23ttQ/\nl16q+vjjqrNm5XBmm6EfksmUSzorwJdjMS9dRGQ8cCBwRhrtNiMi3YG9sCWqA4HzReR0oBC4WFWX\nA52ADyPN5gUZwNwy8n2xDcxWqO2MWbZ+p1gbVd0kIitD/YiXN4jIYGAwQNfaljgyV+RB1ubLL4er\nrio9r260/bRpZqAfP94M9r/6Fdx9t0XJJ9q9cdIk8/x66ilzNT77bLj44uzECji1D1X49tvyMSSL\nF8fXa9nSdg2N2UgKCswluNbsCBqzq+TKW0xVJ4rIZGwHSsFsJ0sqaLYZEWkJPAVcqKqrRORO4F+A\nhs8bgD9UZfDVRVVHA6PB0r/kYgy1jqqkxs+iO2OUtWtLMwXHWLIE2ratfF/FxeauPGqUbQXcvDkM\nGmTZiHv1Kl9f1eqNHGnR91tvbVsJX3BBfKCmU7coLoZvvimvSKIxJGBOHQceGK9IdtyxFimSZAwc\nmJX/q5A6/UufMqIF4bOriHRV1ckVdS4ijTHFMl5VnwZQ1YWR6/cAMa+z+ZgTQIzOQUYS+VKglYg0\nCrOXaP1YX/PCRmdbh/pORYwYEe+eCKlT42fZnXH1avsFWFgIe+9dKu/Xz2JHoq7H6bBypXly3Xab\n/frs0sVchf/4x8SxACUl8OyzplQ+/tgUybXXWtR9toPQnJpl40bLrBBVIlOmWOaFKM2awX77mbdW\nTJHsvLN7ApYj0VqZLaPxRigfABuxJaxJ4fiDZO0i7QUYC9xcRt4xcnwR8Gg47k28QX8WZsxvFI57\nUGrQ7x3aPEG8QX9IOD6PeIP+4xWN120uEWLeYmDGhJjNJdFabJZsNDNmlHY1aFB81yNHVn6d+quv\nVM87T3WLLayPgw9WfeIJ1Y0bE9dfv171gQdUd97Z6m+3nepdd6muXVutx3JqCWvX2lbWd9+tes45\nqnvvrdq0afk/48aNVQsKVP/0J7PrTZni2x6UhWoY9J8Gdouc7wo8mUa7g7Clr8+IuB0DDwHTgvy5\nMspmOOYZ9hVwZEQ+APM2mwkMj8i3Az7GXI6fAJoGebNwXhSub1fReF25lCFdY1+G3BmjnHxy4i7B\nDKbpUlKi+tJL5tIJts/8oEGqkyYlb/Pjj6o33aTaubNu9kB79NHkSsip/axerfree6q33qp65pn2\nb9qoUfm/rUaNVPfcU/WPf7QfEoWFmXVrr6tUR7nMSEeW78WVSxlSzUiicTCxmU3Z0ratleh5BV4o\nxcXJlUqDBvarMR1Wr1a9447SWcfPfqZ65ZWqP/yQvM2SJapXXKHapo21+fnPzYXUY1Tyi+XLVV9/\nXfWGG8x7b5ddEv/+adBAdbfdTNncfrvFM/mstGokUy7prBJ+JiL3AuPC+cAw63DqMsm8w2I2lZiN\npTjBtj6NG5txI2p5X7rUNq2HhLaYshH2Ufr2NeN7RdHIs2ebLeW++yyBZEGBZSk+8cTktpl58+CG\nGyzafs0aOOYYS3l/wAGp7+XknsWLyxvaZ80qX0/EnDSiNpI99yyfWcHJLCk3C4PNG4adC/w8iN4G\n7tQ6treLbxZWhnbtTCGURcR+/JWlYUOzfnftalb4RG3BsurNnh0nOvVU8wtIxIkn2l4nyXJxqVqm\n4VGjzPAuAiecYF5c+++ffKOtL7+E666z/elLSuCUU0yp1MQGYk7lULX0O2UVybx5ievvuGO819ae\ne1oGaic7VHqzsBhBidwUilPfSfZjpKTECqT2v4zMiNatS53A8fLLrSRSEOvWWVLIUaMsK2zbtqYc\nhgyxZH7J+OQT8/x65hmLUTnnHNtHpVu35G2cmkPVfnuUVSSLFiWuv/32pXm2YhmA3YuvduDOc05i\nli2rXP1oHEyyWJlIvUmT7GWQiMaNYcwYOPnk8te+/x7uvNOCHBcvhl13hXvusZW2VLOb116zfVRe\nf93S3A8fDn/+s6W/d3JDSUniGJIVKxLX79YtfkaSMpW8k3NcuTiJSaYg2ra1aMZUcTAjRtjWitHc\n4GBaY8QIrrkG/v73xLfdZhtb3tpvv3j5Rx/ZLOWJJ8zMc/TRtnfKoYcmX/oqLrYZysiRpsw6drQt\nhc85x5dJappNmxLHkKxenbh+587xM5K+faF9+5ods1M90lYuIdIeVU3y5+DUKZIFU44aZcdDh5ba\nVcpOGWIG+2idtm2Zf/loOp96fNJb7rabBUbGlqg2boQnn7RbfvSRReeff76VVNsBr19vtpTrroOv\nv4YddrDZzWmnxe8M6WSH9evNQSOqSD77zJYyE9GhgwXIZiyVvFM7SORCFi3AbsCnwBzgOyyQcteK\n2uVbcVfkBCRLvV+FhHd9+yZ3MwbVo45SXbXK6i5apHr11arbbmvXevZUveWW0uvJWLVK9frrS9vt\ntZclB9y0KSPfhpOA1atV339f9bbbVP/wB4sTSRRDEivRVPL//a9llHZ37/yGJK7I6XiLvY8FLr4R\nzg8B/q2qdcpZ073FKkH37omXzBJ4gqlWnF/p4ostpcqMGaUJJNevtxQvQ4dC//6p+1i8GG691dyQ\nly+3pbJhwywBZbIlM6fyrFxZfkOrL78s9eMoS+vW8TaSggJLt+P/JnWLKnuLAVvEFAuAqr4pIltk\ndHROfpFm5uRXXoEjjkhcdZttzGfg1lttWeTww+Gtt2zl7cwzzdieKIFk2dvdcIMtea1dC8cdZ0pl\n332r8ExOHEuWwKefxiuSoqLk9bfaKl6J9O1r2zq7Iqm/pKNcZonIP7G0LQCnYrm+nNpKRVmKq5vF\nOI3MycleKl26mBln8WIYMMBmLLNnW9PrrrMEkq1bp779559bu4cftvNTT4VLL4Vddkn/EZxSFiwo\n77GVaoeFli3NU6vWppJ3ageJ1sqiBWgN3AJMxuwtNwOtK2qXb6XO2FwS2UOaNLH0KyL22aRJpewl\n5Wwv556b1OaSKoXLKaeUlx18sOqTT6aXu+uDD1SPPbb0dkOH2q59TnqUlKjOnq369NOq//iH6oAB\nlhonlS2seXPVAw+073rsWNXPP3cblhMP1bC5NAY2hU4QEcF2f9yYRZ1X49QZm0sye0hFJLCXAOVT\n6oOtXQ0aBC+8EDf7eantQI48Mnn30WGdcYZF0e+1V+phqdry2siR8OabNqv585+ttGtXyWesR5SU\nwMyZ5WckqcKXmja1aPbojMRTyTsVUR2by9tAP+DHcL4l8GLYSbKxqn6ZuWE61aaqO0Ymazd8eLxi\nATt/4YU4ZVTR2npMsfzzn+ZKXFHwYnGx7fQ4cqSt/XfqBDfeaLs+tmyZum19Y9Mm+OqreCXy6afw\n44/J2zRuDHvsEW8j6d3b5I6TCdJRLs1VdfOfqdpukt2B3wJnAz2zMzSnSqSKjq+oXSIqMN6XlFha\nsYooKID33qt4c6/16y06/z//MQPyjjtaIsqBAz1GBez7mTGjVIFMnmzpb9auTd6mUSPLZBCdkey6\nq3+fTnZJR7n8JCJ7qOpUABHZC5irqteJiCfPqG0kCn6siFQ7TaYw3j/wgAXiV8T//gdHHZW6zqpV\nltLlppvMwNy3x1KebDec476+l4ZXdYam2dk6uTazZo0FH0ZnJNOnW3BpMho0sBlIVJHsvrvtnug4\nNUoiQ0y0AHtjm269DbwDfAP0rahdvpU6Y9BXjTfAt21r2+mV3V4vZuBPtsNktK8ExvtURuCddrLP\nrl1Vp05NPdSFC1WHD1dt1craHH646qvDJmpJ88oFaeY7K1eqvvWWbVR22mmqvXvbniOpvmcR26/k\ntNNUR42yDbF++inXT+LUN6jqZmHWlsbYDpS7YnaWnCuDTJc6pVzKkizSvgrtS7p2S/qy23NPi7wG\n1f32S70517ff2rbDzZrZsH77W9WPPw4Xs7R1cm1hyRLViRNVr71W9fe/twwEqZRIrOy4o3nc3Xij\nKaKKMhY4Tk2QTLmk4y3WAvgL0E1VzxaRnsBOqjohO3Op3FBnvMWyyPPPJ1/eeu89W9YaO9ayGd9/\nf+KlmOnTLUblkUdsCee00yxGZaedIpUaNEic2l8keTh4LeWHH8p7bKVjEttuu/IZgD2VvFMbqY63\n2ANYfMv+4Xw+tj99SuUiIl2AsUAHQIHRqjpKRNoAjwHdgdnAiaq6PLg4jwIGAGuAM1R1cuhrEPCP\n0PXVqjomyPsCDwLNgReAoaqqye6RxrM6SUjlDfbZZ7aPyrvvwlVXwT/+Ub7+++9byvsJE2CLLSyt\ny0UXJdl7JY0gzdqGKsydW16RLFhQcVtPJe/USRJNZ6KFMOUBPo3IpqbRriPQJxxvCXwN9AKuA4YF\n+TDg2nA8AHgREGA/4KMgb4NlBGiDBXTOIgRxAh+HuhLaHhnkCe+RquT1slh1l70q6DfVvvbvv6/a\no4ctbz32WHzzkhLVF16wQEkwM8+VV9qyUIX3rWRizJqkuFj1m2/sef/2N9Vf/cqeLZ2lrU6dLBD0\nX/9SffFFS9LpOPkMVbW5AO9jM4PJ4Xx74OOK2iXo51ngV8BXQEctVUBfheO7gZMj9b8K108G7o7I\n7w6yjsCXEfnmesnukarkrXLJ5Iu4jCPAT423TvqSPPhg1WeeUd1qK4vy/uij0m42blR9+GHV3Xe3\nup07q958s2XQrdJYMqkwK8mmTaozZqg+9JDqRRep/uIX9szpKJIOHSzb8xVXqE6YoLpgQU4ewXGy\nSjLlks6y2OXAS0AXERkPHAickUa7zYS4mL2Aj4AOqhpbLPgBWzYD6ATMjTSbF2Sp5PMSyElxj7pH\nsiDH4cMr57pbJhL/0qWX8h8uTVj19INmsdf0cfz2N8PZrfFX/O9fX9Fln9+wbp3td/+f/8CsWZbr\n68EHzQZTUXxLOQYOrHHX4w0bLG9ZdFlr6tT0vLrbtSufAXjbbT1xo1N/Salcgh3kS+B4Spefhqrq\nknRvEDYZewq4UC0Ac/M1VVURSe1RUE1S3UNEBgODAbrW4vX8lCQLcpwzx1LBpJucMqKkhOT/JBcw\nio0fNueiTf/HMTzL+I0DKR62Jde+352b3tyLhQthn30sW/Exx9TeZIZr18K0afGKZNq08ptnJqJs\nKvm+fe0rdkXiOKWkVC7hxfyCqu4GPF/ZzkNesqeA8ar6dBAvFJGOqrpARDoCi4J8PtAl0rxzkM0H\nDikjfzPIOyeon+oeZZ9vNDAazFusss9XK0hm/BYplc+ZY7MSSK5ggpJKpVj+xkg+pQ+vbOrHJVzH\nhdzMCIZzx9ohrHpsa/r1s5T3hxxSu160P/5oM5CoIvn8c0sxUxFbbVW61W6seCp5x6mYdJbFJovI\n3qr6SWU6DrOe+4AvVPXGyKXngEHAyPD5bER+vog8CuwLrAzK4WXg3yISS8TeD7hMVZeJyCoR2Q9b\nbjsduLWCe9Q9EkXki5R35V2zxpJNQkIFU9ylO42+S7yTglDCpVzHs3IcM3U7/sG/WEpbtmMWG2jC\nCTzJMK6lz8uTMvVUVWbZMkuLEt2L5OuvE3s2l8VTyTtOBklkiIkWbFmsGJgJfAZMAz5Lo91BmAvy\nZ8CUUAYAbYHXsEj/V4E2ob4At4f7TAMKIn39AcsSUAScGZEXANNDm9tgc9xOwnukKnlr0Fctb/xO\nZWVOYOx/7rnk1ZuwTi9glLZtsNS8nRp+rw3YpE1Yp2dzt37NDqWuYDXMDz+YN9rVV6sef7xq9+7p\nGdpjqeQPOED1ggs8lbzjVAeqEUTZLYlSqkJ2xNpLnQqirCjtfiS9frLlnYZsonkz5bgTGjNuXKm8\nZbON/GnDrVxUcj3bEgniaNwYHnggc0b4yIZm2qUr8y6+icndfhO3tPX996XVGzWyZa5Ef86eSt5x\nskeyIMqkykVEmgF/AnbAZhL3qeqmrI4yh9Qp5ZJoD5YoIdI9mWJp0cK8u1asKJU1agSXXw7nnQet\ne7aDpUvLN0y2J0wlUIVZNz3L5MueYPKG3kymD5PpwxLaA7ZEtcUWFqgfK8XFlnYeTMftvnu8IvFU\n8o6TPaoSoT8G2IglqzwSC4Acmp3hORklNnsYNCix1bpr16SKRcR0UlQv3XgjnHOOKR0g+Y5TldxL\nprjY7CFl9yFZufJY4FgasZG2LEVQ2rKEkgaN2NC81eZ9Sho2hN1281TyjlMbSaVceql5iSEi92HR\n8E6+EFMwZWYwPzVvR8s5s5M2i05k997bcoaV+9VfhfQsGzeWjyGZMqV0aE2bQseOlj9ry5VzKaEB\n62nKQn4GQAOK6VXyOQW/axWXSr5581RfguM4uSKVctm8a4SqbhL3vcwvYjaLNWvsJ35xMaPbDOOc\nZdek1fz552HAgCQXE3moRfaEWbcucQzJ+vVWdYstTA917Vq6tLV6demKmtCJnfmSAgo3lz2Yyhbd\n2sMDs6v0dTiOU7OkUi57iMiqcCxA83AuWAjMVlkfnVM5Ygplzpx4d+TiYotfSbF/eozu3W1zr113\nTVEpNisaPpzVc5YytUM/Jh8xjMmv7s3k62ynxNhqXKtWluG3V69SRbJyJXzxRWl3O+4Ihx5aurS1\n19dPsOXQPyRVXo7j1H4q9BarL+S9QX/8eDjzzITbFKYKjIxywAHwzDPJ97dfsSI+fmTyZNu7PfYn\n1L499OxZmhm/pASWLIGZM0v7SDuVfMRbLK0MA47j5IRKe4vVN/JeubRL7MGVrmI59VS4557SPVgW\nLSqvSGZFYiw7d7Y9WKKK5Pvv4ZtvSpVN167l06R4KnnHqVtUZz8XJx+ohmIZMgSOOMI28YopknmR\nlKDbNZrDLpumsWvzppTsvAvFP+vMd9/BG2+U7t3VqZMpkNNOK1Uk7dtn4sEcx8lHfOYSyPuZS8Th\nopgGNCKNxFkJuth5Z7OPNGoEJd/OYdOkKcws7s4MelMcfot02HotBQc1j5uRdOyYsSdxHCeP8JlL\nXadtW1i6lJVsRStWpt1sIONo0rAEPeBANnTZni+/hOeei5luutGOFhRQyDE8t9lza9utGyMTZld9\nrG5PcZw6jyuXusKoUXx4xl3sv+mdtKr342WW05on+B0bipvCO6Wp5C++ONhJTuhOV+ZQzgl9bjXc\n0stmD0gnY7PjOHmHL4sF8n1ZbMwYOOOM9OtvxUr6MikSSzKJHiUz4yP3k+Uoq06al2z06ThOzvBl\nsTrME0+kr1geansh+yx9gR0ookHU4N+tG+WmKBUES1aJZCliKpk6xnGc2o3vVJHn/Pe/cOKJFdd7\n8EFzET511N7s2GJ+vGJJpjAGDoTRo4PiEfscPbp6y1fJUsTk606gjuMkxJVLHvOvf8FvflN63qSJ\n7ZJYlrffLt0nrNIKY+BAW64qKbHP6tpFRoyIZMAMePS949Q5fFksTzn0UHjzzdLzfv0sEj4aDd+x\nI7z7rkXFxzFwYO6M55HUMe4t5jh1F5+55CEi8YrlhBMsdX1UsRx8sOXv2qxYxo83Y3qDBvY5fnzN\nDbgsmZ4NOY5T63Dlkmf86U/x52f9oogPn/k+ztHqnHPg9dcjObti7r9z5pjhJeb+m0sF4zhOncaV\nSx6xYgXcfXfp+dAjvmTCW1syr3jbzbJbGl/MXQePj9/CN5Z6P8qaNSZ3HMfJAllTLiJyv4gsEpHp\nEdkVIjJfRKaEMiBy7TIRKRKRr0TkiIi8f5AViciwiLyHiHwU5I+JSJMgbxrOi8L17tl6xppkxQoL\ncozx97/DuInbsJAOm2UvcQR/3nijWe+js5JU7r+1abnMcZw6QzZnLg8C/RPIb1LVPUN5AUBEegEn\nAb1DmztEpKGINARup3Sb5ZNDXYBrQ187AMuBs4L8LGB5kN8U6tUcWXhZl1UsV10Ft98OS0tKUwzP\noBdH8IqdFBfHL3slc/Nt06b8ctkf/mAZll3ZOI5TDbKmXFT1bdLangqAY4FHVXW9qn4LFAH7hFKk\nqrNUdQPwKHCs2LaYhwFPhvZjgOMifY0Jx08Ch0tNbaOZBdvG8uXxiuXaa62sjKQPW0w7evFFfMPo\nslcy999YvSgbNliGZbfNOI5TDXJhczlfRD4Ly2ax12YnYG6kzrwgSyZvC6xQ1U1l5HF9hesrQ/1y\niMhgESkUkcLFixdX/8kybNtYtix+/5MbboBhw+Cnn+x829ZrWNe8Ne0on24fKF0OSxbbsiwN3e+2\nGcdxqkBNK5c7ge2BPYEFwA01fP84VHW0qhaoakH7TGw+ksHUJsuWWaLjGDfeaAklY6ngfvMbmLe0\nBU3vuQ0aNkzcSXQ5LJH7b7pR8Z6apfq4bcupZ9SoclHVhaparKolwD3YshfAfKBLpGrnIEsmXwq0\nEpFGZeRxfYXrW4f62SdDqU2WLo1XLNdfD3/5S+n5iBHw9NNhC5eBAy1rZVWi3hMtlyXCU7NUD3cF\nd+ojqpq1AnQHpkfOO0aOL8LsLGCG/KlAU6AHMAtoiGUQmBVkTUKd3qHNE8BJ4fguYEg4Pg+4Kxyf\nBDyezlj79u2r1WbcONUWLVTtFWKlRQuTp8nixfHNr702/vyZZ1Lcu1s3VRH7THXPaN22ba3Ejhs3\njr+hiH1W1KeTnG7d4r/TWOnWLdcjc5xqAxRqovd/ImEmCvAItvS1EbOJnAU8BEwDPgOeK6NshgMz\nga+AIyPyAcDX4drwiHw74GPM+P8E0DTIm4XzonB9u3TGmxHlolq5l3wZyiqWq66KP58yJUPjS6UA\nY+OPKhZXNNWj7PcY/T4dJ8+pceWSbyVjyqWKLFwY/97561/jz3/4Ic2OKlJu6f6KTlavijOyeo3P\nXJw6TDLl4hH6tYAFC6BDaSwkp55qdpYYa9bEXy9HzFgsAqedlnptP12ng4qM+O5Flj6eCdqph7hy\nyTHffQfblmZv4YADYNw4O27WDDZtgubNU3QQNRZDqTtZjLJKIF2ng3SM+O5Flh7Z2BfHcWo5rlxy\nyDff2HsmxlZbwfvv2/HBB5teSOZlvJlEsTVliSqBdH9Fp+NJ5l5k6eOZoJ16hiuXHDFtGuy4Y7xs\n1Sr7PP982+ArrbwC6cweysa7pPMrOloPyg/Gl3Ucx0mBK5ccEY1biTJqFNx6ayU6qmj2kEgJpPsr\nOlZPFR56yJd1HMdJG1cuOWDZMtvIqyxPPQUXXFDJzhItX8VmGZlUAr6s4zhOJXDlUsOsWgX9+8P8\n+fHyN9+E44+vQoeJlrkeegjOPRfmzTPXs0aNYMiQTAzfcRwnLVy51CA//QRHHQWffBIvnzwZfvGL\nanRcdlbx3ntw552Weh/s8847XcE4jlNjuHKpIdavt2ST77wTL//6a9hrrwzfbPToysnTxZMvOo6T\nJq5caoCNG+H3v4eJE0tlHTva0ljPnpXoKN2Xe2zGkq483Xt78kXHcdLElUuWKS62XYeffbZU1rcv\nTJ8eHzxZIZV5uScLjqkwaCYFGd6rxnGcuo0rlyxSUgLnnAOPPFIq698f3norfhOwtKjMy33w4MR9\nNGtW9ZlGBveqcRyn7uPKJUuowkUXwX33lcpOPtlmMFtsUYUOK/Nyv+MO8xZrUOaf96efTPEMGVJ5\n20mG9qpxHKd+4MolS/zzn3DLLaXn559vOcOaNKlih5V9ud9xB3TpUl6+Zg3cdVflbSeefNFxnErg\nyiUL3Hpr/Dv3iitM0ZSdSFSKqrzck812KkpumQhPvug4TiVoVHEVp7K88Ubp8W23wXnnZaDT2Et8\n+HBTGl27mmJJ9XLv2rU0W3JFpGM7GTjQlYnjOGnhM5cM89ln8O67FhT/8MMZUiwxKpuCJVVqmLK4\n7cRxnAziyiWDFBbCoYea3fx//zMDfk5JtJT1pz+57cRxnKzjyiVDvP8+HH64mTNefdVcjmsFZWc7\nd9zhthPHcbJO1pSLiNwvIotEZHpE1kZEJorIN+GzdZCLiNwiIkUi8pmI9Im0GRTqfyMigyLyviIy\nLbS5RcTWe5LdI5u88Qb06wctW1p6l/33z/YdK6CiSH7PcOw4TpbJ5szlQaDs7/dhwGuq2hN4LZwD\nHAn0DGUwcCeYogAuB/YF9gEujyiLO4GzI+36V3CPrPDBBzBggKVzee896N07m3dLA0/T4jhOLSBr\nykVV3waWlREfC4wJx2OA4yLysWp8CLQSkY7AEcBEVV2mqsuBiUD/cG0rVf1QVRUYW6avRPfICvPm\n2XLYu+/aJCHneJoWx3FqATVtc+mgqgvC8Q9Ah3DcCZgbqTcvyFLJ5yWQp7pHOURksIgUikjh4sWL\nq/A48LvfwYQJ0CHpXTJMRUtenqbFcZxaQM4M+mHGoRVWzOI9VHW0qhaoakH79u2zOZTMkM6Sl6dp\ncRynFlDTymVhWNIifC4K8vlANFdJ5yBLJe+cQJ7qHvlPOktenqbFcZxaQE0rl+eAmMfXIODZiPz0\n4DW2H7AyLG29DPQTkdbBkN8PeDlcWyUi+wUvsdPL9JXoHvlPOktenqbFcZxaQNbSv4jII8AhQDsR\nmYd5fY0EHheRs4A5wImh+gvAAKAIWAOcCaCqy0TkX0BsY+CrVDXmJDAE80hrDrwYCinukf8kS+dS\ndsnL07Q4jpNjRMsmKq9q8wAACI1JREFUMaynFBQUaGFhYa6HkZqYzSW6NNaihc9MHMfJGSIySVUL\nyso9Qj+f8CUvx3HyBFcu+Uauo+srcoV2HMfBlUv1qG8vWo/+dxwnTVy5VJX6+KL16H/HcdLElUtV\nqY8vWo/+dxwnTVy5VJX6+KL16H/HcdLElUtVqY8vWo/+dxwnTVy5VJX6+KJ1V2jHcdIkaxH6dZ7Y\nC3X4cFsK69rVFEtdf9F69L/jOGngyqU6+IvWcRwnIb4s5jiO42QcVy6O4zhOxnHl4jiO42QcVy6O\n4zhOxnHl4jiO42Qc388lICKLsc3FytIOWFLDw8kV/qx1E3/WukltedZuqtq+rNCVSwWISGGijXDq\nIv6sdRN/1rpJbX9WXxZzHMdxMo4rF8dxHCfjuHKpmNG5HkAN4s9aN/FnrZvU6md1m4vjOI6TcXzm\n4jiO42QcVy6O4zhOxnHlkgIR6S8iX4lIkYgMy/V40kVE7heRRSIyPSJrIyITReSb8Nk6yEVEbgnP\n+JmI9Im0GRTqfyMigyLyviIyLbS5RUSkZp9w8zi6iMgbIvK5iMwQkaFBXheftZmIfCwiU8OzXhnk\nPUTkozC+x0SkSZA3DedF4Xr3SF+XBflXInJERF6r/t5FpKGIfCoiE8J5nXxWEZkd/samiEhhkOX/\n37CqeklQgIbATGA7oAkwFeiV63GlOfafA32A6RHZdcCwcDwMuDYcDwBeBATYD/goyNsAs8Jn63Dc\nOlz7ONSV0PbIHD1nR6BPON4S+BroVUefVYCW4bgx8FEY1+PASUF+F3BuOB4C3BWOTwIeC8e9wt9y\nU6BH+BtvWBv/3oG/AA8DE8J5nXxWYDbQrows7/+GfeaSnH2AIlWdpaobgEeBY3M8prRQ1beBZWXE\nxwJjwvEY4LiIfKwaHwKtRKQjcAQwUVWXqepyYCLQP1zbSlU/VPvLHRvpq0ZR1QWqOjkc/wh8AXSi\nbj6rqurqcNo4FAUOA54M8rLPGvsOngQOD79YjwUeVdX1qvotUIT9rdeqv3cR6Qz8Grg3nAt19FmT\nkPd/w65cktMJmBs5nxdk+UoHVV0Qjn8AOoTjZM+ZSj4vgTynhKWQvbBf9HXyWcMy0RRgEfbymAms\nUNVNCca3+ZnC9ZVAWyr/HeSKm4FLgZJw3pa6+6wKvCIik0RkcJDl/d+w70RZD1FVFZE644MuIi2B\np4ALVXVVdEm5Lj2rqhYDe4pIK+AZYOccDykriMhRwCJVnSQih+R6PDXAQao6X0S2ASaKyJfRi/n6\nN+wzl+TMB7pEzjsHWb6yMEyRCZ+LgjzZc6aSd04gzwki0hhTLONV9ekgrpPPGkNVVwBvAPtjyyKx\nH4nR8W1+pnB9a2Aplf8OcsGBwDEiMhtbsjoMGEXdfFZUdX74XIT9aNiHuvA3XFNGq3wr2KxuFmYI\njBn9eud6XJUYf3fiDfr/Id5AeF04/jXxBsKPg7wN8C1mHGwdjtuEa2UNhANy9IyCrSHfXEZeF5+1\nPdAqHDcH3gGOAp4g3sg9JByfR7yR+/Fw3Jt4I/cszMBdK//egUMoNejXuWcFtgC2jBy/D/SvC3/D\nOf3Dqe0F88z4GlvbHp7r8VRi3I8AC4CN2BrrWdga9GvAN8CrkT88AW4PzzgNKIj08wfMCFoEnBmR\nFwDTQ5vbCJkecvCcB2Hr1Z8BU0IZUEefdXfg0/Cs04H/C/LtwsujKLx8mwZ5s3BeFK5vF+lreHie\nr4h4DtXGv3filUude9bwTFNDmREbS134G/b0L47jOE7GcZuL4ziOk3FcuTiO4zgZx5WL4ziOk3Fc\nuTiO4zgZx5WL4ziOk3FcuTj1EhFpG7LQThGRH0RkfuS8SQ2PZV6Ius9EX++KyJ7ZqC8ifUSkf9VH\n59QnPP2LUy9R1aXAngAicgWwWlWvz+mgaj99gF2Bl3I9EKf24zMXxymDiPwvJBGcISJ/DLJGIrJC\nRG4M8pdFZF8ReUtEZonIgFBvexF5J+xDMklE9g3yX4rIayLydNhHZGyZ214Y2nwmIjuGNu1E5Lkg\ne19Edk0w1hYi8oSIfCEiT2EBhbFrp4Z9PKaLyL8r8fzNRWRMaDtZRH4uIs2B/wMGhtndCSLSUkQe\nFNtn5lMRObqy37VTd3Hl4jjlGaSqfYG9gb9I2KgJy1n1oqr2BjYAVwCHA78Drgp1FgC/UtW9gIHA\nLZF++wDnY/uM7CIi+0WuLQxt7sX2MQH4F7Zfx+7hXg8mGOv5wHJV3QW4GssMHUtZfzVwaJAdGBJC\npsMFwHpV3Q04DXgIKA7POF5V91TVJzFl85Kq7oPl/7pBRJol69SpX7hycZzyXCQiU4EPsER/2wf5\nWlWdGI6nAW+qpXifhuVyA8tjdZ/YLqCPYookxoeq+r1aduMpkTYAsaSbkyLyg7AXO6r6CrCtiGxR\nZqw/B8aFOp9iKUQA9gVeV9UlqroR23Tr52k+/0GRPmcA3wM7JKjXDxgetgF4A5s1dU3zHk4dx20u\njhNBRH6JvYT3U9W1IvIupUtNGyJVS4D1kePY/6WLsX01TsU29FodabM+clxM/P+/9UnktRkBjlPV\nmbkeiFP78JmL48SzNbAsKJbe2NJYZdsvUEvaNwh7AVeVd7CltZjSm6+qP5Wp8zZwSqizB5YJGGzT\ntEODV1wjLFvwW1W47y7YdtJFwI/YdtIxXgb+HDsRkb3SfjKnzuPKxXHieR5oISKfYzaLjyrZ/jbg\nj2FZrQfxs5XK8n/A/iLyGWbvODPJ/dqKyBfAP7HMyajqvHD+JrYE96GqPp/kPv/f3h3TIBSDYRS9\nHQliMMOMFzSggaCBBRMEB/iA4T0J/3iOiZvma9rnfh36u9Z6VLfqsNZ6V/fq8tu+A35Vp328P1fX\n6rgP/5+2XQiqvIoMwDwnFwDGiQsA48QFgHHiAsA4cQFgnLgAME5cABj3B3CdQ8sHoDooAAAAAElF\nTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "fB8cnF96qcQM"
+      },
+      "source": [
+        "###  Modelo - Árvore de Decisão "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "sgo7wyZUqcQQ"
+      },
+      "source": [
+        "Treinando o modelo de Arvore de Decisão  com o Conjunto de Treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "2f83d111-579f-4617-bbc7-4d1441914581",
+        "id": "-T3_UnXvqcQR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "\n",
+        "regressor = DecisionTreeRegressor(random_state = 0)\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
+              "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+              "                      min_impurity_split=None, min_samples_leaf=1,\n",
+              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+              "                      presort=False, random_state=0, splitter='best')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "isKict8VqcQW"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "dc307c8a-b1b9-4c8e-cb8e-f49e749e050d",
+        "id": "myt4FBHpqcQX",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([195000., 115000.,  94000., 216500., 139400., 113000., 214900.,\n",
+              "       402000., 195000., 150750., 148000., 215200.])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "y0HxOT1nqcQa"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "b802e761-2c5c-465b-ca55-f42f5e2fd0a5",
+        "id": "EXTaMCEuqcQb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-1.1563461633885121"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "-8m22r5SqcQd"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "41bc6a12-484d-483c-8221-ed9b43f54116",
+        "id": "HTJnv_R9qcQe",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_reg(X_train, y_train, regressor, 'Arvore de Decisão (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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2Ss0qlAMPhMcfB4Z/LQyFpVNTA48vaAy++GJIU3bEbY4zytNo8x98wABY16+Z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\n4zgFx5WL4ziOU3BcuTiO4zgF5/8DyR4QJtjHHvsAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "XviIofWNqcQj"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "7c7c2439-349e-401f-d0bc-e9b58d2f1461",
+        "id": "57h8qL4EqcQk",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_reg(X_test, y_test, regressor, 'Arvore de Decisão (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iZjZD0nOEqi0B55nZe8VmIGlr4B7gu2a2RtL1wGUET+gy4Grg680pfEuRNAGY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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "MyrmVTcFtw9L"
+      },
+      "source": [
+        "###  Modelo - Random Forest"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "-qBwfwdatw9N"
+      },
+      "source": [
+        "Treinando o modelo de Random Forest com o Conjunto de Treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "d1891c7a-7e6c-4100-9d7d-0e3710b5f410",
+        "id": "2G9vzxcatw9N",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "\n",
+        "regressor = DecisionTreeRegressor(random_state = 0)\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
+              "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+              "                      min_impurity_split=None, min_samples_leaf=1,\n",
+              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+              "                      presort=False, random_state=0, splitter='best')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "fl4bh74Ttw9T"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "ce1fa1f1-cae6-4b89-d8c3-acbac1577ecc",
+        "id": "v4a4FEGktw9T",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([195000., 115000.,  94000., 216500., 139400., 113000., 214900.,\n",
+              "       402000., 195000., 150750., 148000., 215200.])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "avqChi46tw9W"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "8b77d5e2-6ccd-4026-e7af-f022d699efd3",
+        "id": "XCRy0Ekktw9X",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-1.1563461633885121"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "iykUlZD0tw9d"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "e69ea18d-464a-4bae-85e6-01217c536f86",
+        "id": "QPK8C0Fgtw9e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "plot_results_reg(X_train, y_train, regressor, 'Random Forest Regression (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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odDH8kXYc4K674PK4XZWK2UfQvpRbvo7Gjt6WooidBsceC4cfXlwYx+ksuG0xxwG2xx3S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\nXLk4juM4qePKxXEcx0md/w+cUvYyu3QqiwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "frp-AsLstw9l"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "fd1587db-da4b-40d1-c3ea-3f1faad27bee",
+        "id": "hgES4om6tw9m",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "plot_results_reg(X_test, y_test, regressor, 'Random Forest Regression (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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M7FmgkbDszEig0cwWmtmbwFRgrCQBo4FbY/zJwMmJtCbH81uBo2P4uqASlkux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\ncnEcx3Eyx5WL4ziOkzmuXBzHcZzM+f8HxrgJdLSR2QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "Ood5PUZCtxsa"
+      },
+      "source": [
+        "###  Modelo - SVR"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nd_nx5-ju7Wh",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalização das features:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "isa4YR-Mu7gd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X_train = feature_scaling(X_train)\n",
+        "X_test = feature_scaling(X_test)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "TYZy4UgYtxsc"
+      },
+      "source": [
+        "Treinando o modelo SVR com o Conjunto de Treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "435c97fb-d4c5-446e-8738-6e10dd173e4f",
+        "id": "OSS7HBwstxsd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "from sklearn.svm import SVR\n",
+        "\n",
+        "regressor = SVR(kernel = 'rbf')\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+            "  y = column_or_1d(y, warn=True)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
+              "    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
+              "    tol=0.001, verbose=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pjZ3LL7jvCv2",
+        "colab_type": "text"
+      },
+      "source": [
+        "Prevendo os resultados com o conjunto de testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "1e4971fd-6234-47a4-f9df-50b66f96884b",
+        "id": "HvFjHJ76txsl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([194251.44370433, 194247.70187487, 194245.70018159, 194247.1367784 ,\n",
+              "       194248.45357491, 194249.01439172, 194250.67281124, 194249.47673342,\n",
+              "       194251.37775764, 194246.60246747, 194249.61450541, 194248.02383322])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 39
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "UnULaDnVtxsr"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "881ac17f-0128-4014-a6a9-4b04e0ce0c63",
+        "id": "ULsy2S4Htxsx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-0.041556703288624375"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "T4L09wqRtxs2"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "2ee9f048-a7a4-43e0-a204-46b9f25aeb40",
+        "id": "4dRGKOeXtxs4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "plot_results_reg(X_train, y_train, regressor, 'SVR (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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A2Bc4HliWN1sGnJA/Hw9cFclKYA9J+wDvBFZExMaIeBpYASzM66ZHxMrcwfOq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\nM7OG+/8rBrr9cW3qtAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "d91HUwEXtxs-"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "62ad4c35-8e5a-4f4b-ea1c-5fa2d896115e",
+        "id": "OoqXeIfBtxtA",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "plot_results_reg(X_test, y_test, regressor, 'Arvore de Decisão (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1wAjggoiYl3fxReASSWcCdwHn5/LzgZ9I6iZdUHBMo15jQ1V+nX/qqakpbOzY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\nk4uZmZXu/wP4AUFGM0WJIwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "VUZAwJjMvh0r"
+      },
+      "source": [
+        "###  Modelo - Regressão Linear Múltipla"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZOIXAi6Wvpce",
+        "colab_type": "text"
+      },
+      "source": [
+        "Selecionando as principais features do dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "et8CNkI1vpk2",
+        "colab_type": "code",
+        "outputId": "ce479ea9-3f72-4269-d0f0-ac7139dbe9ce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>661</th>\n",
+              "      <td>46589</td>\n",
+              "      <td>0</td>\n",
+              "      <td>711</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1994</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>402000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>964</th>\n",
+              "      <td>11316</td>\n",
+              "      <td>0</td>\n",
+              "      <td>510</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>214900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>891</th>\n",
+              "      <td>11184</td>\n",
+              "      <td>0</td>\n",
+              "      <td>440</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>172500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>926</th>\n",
+              "      <td>11999</td>\n",
+              "      <td>0</td>\n",
+              "      <td>656</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>285000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1374</th>\n",
+              "      <td>10625</td>\n",
+              "      <td>0</td>\n",
+              "      <td>936</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2005</td>\n",
+              "      <td>FV</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "661     46589         0         711            7       1994       RL     402000\n",
+              "964     11316         0         510            5       2002       RL     214900\n",
+              "891     11184         0         440            5       1978       RL     172500\n",
+              "926     11999         0         656            5       2003       RL     285000\n",
+              "1374    10625         0         936            5       2005       FV     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZjC2LJYh1SQ6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
+        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SeWh_rpnYkLd",
+        "colab_type": "code",
+        "outputId": "3ab29a73-ecb7-4b37-efc1-b1f41db60644",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "df['MSZoning'].unique()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['RL', 'FV', 'RM', 'RH'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eumBvAB8wbq-",
+        "colab_type": "text"
+      },
+      "source": [
+        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rjT1ONG7wb9x",
+        "colab_type": "code",
+        "outputId": "26d1bcf1-6143-4e6c-a7b2-e7820292f635",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 224
+        }
+      },
+      "source": [
+        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "      <th>MSZoning_RH</th>\n",
+              "      <th>MSZoning_RL</th>\n",
+              "      <th>MSZoning_RM</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>661</th>\n",
+              "      <td>46589</td>\n",
+              "      <td>0</td>\n",
+              "      <td>711</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1994</td>\n",
+              "      <td>402000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>964</th>\n",
+              "      <td>11316</td>\n",
+              "      <td>0</td>\n",
+              "      <td>510</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>214900</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>891</th>\n",
+              "      <td>11184</td>\n",
+              "      <td>0</td>\n",
+              "      <td>440</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>172500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>926</th>\n",
+              "      <td>11999</td>\n",
+              "      <td>0</td>\n",
+              "      <td>656</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>285000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1374</th>\n",
+              "      <td>10625</td>\n",
+              "      <td>0</td>\n",
+              "      <td>936</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2005</td>\n",
+              "      <td>250000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
+              "661     46589         0         711  ...            0            1            0\n",
+              "964     11316         0         510  ...            0            1            0\n",
+              "891     11184         0         440  ...            0            1            0\n",
+              "926     11999         0         656  ...            0            1            0\n",
+              "1374    10625         0         936  ...            0            0            0\n",
+              "\n",
+              "[5 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i9fs5x8MvpxV",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b9uRFW2vvp6b",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
+        "y = df['SalePrice'].values.reshape(-1,1)\n",
+        "\n",
+        "# Dividindo os dados\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "SMaN0iHNvh0t"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "6cbc7612-fa1d-4078-a14f-c2e943dcf113",
+        "id": "VuzaRxyuvh0u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "zT6PUXkWvh0y"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "86f38ad3-ebc8-4b0a-9f8a-fccd2ce0fbd3",
+        "id": "JR-E180Fvh0y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[376052.98497919],\n",
+              "       [185871.16123409],\n",
+              "       [230508.60986049],\n",
+              "       [269278.25223188],\n",
+              "       [153039.2404638 ],\n",
+              "       [ 88676.03962536],\n",
+              "       [210563.82989375],\n",
+              "       [131945.37958412],\n",
+              "       [204656.51558602],\n",
+              "       [226768.14382706],\n",
+              "       [229229.28285228],\n",
+              "       [216857.75545174]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "PO6sGI3Gvh01"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "a5233703-1980-459b-f44f-526e66329bd4",
+        "id": "fQPgnd8dvh02",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7460190735464488"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KDcimzDmxxIo",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Estudo de Caso: Pipeline de Regressão"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g_qAJYI2x4DL",
+        "colab_type": "text"
+      },
+      "source": [
+        "**Descrição do Problema**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JoB6nJpox8AP",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando as packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "C9rs16Mzx8ec",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.svm import SVR\n",
+        "from sklearn.metrics import mean_squared_error, mean_squared_log_error"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VNaNeCy_yEet",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando os dados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OCPXPwhNyEp2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/r4phael/ml-course/master/data/pricing_houses.csv')\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "COuAxCI4yFCb",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando e descrevendo  o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Y-yKSlG_yFRs",
+        "colab_type": "code",
+        "outputId": "f6bb9000-88f4-45b1-9386-2ffeb4f5f31d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "source": [
+        "df.info()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1460 entries, 0 to 1459\n",
+            "Data columns (total 81 columns):\n",
+            "Id               1460 non-null int64\n",
+            "MSSubClass       1460 non-null int64\n",
+            "MSZoning         1460 non-null object\n",
+            "LotFrontage      1201 non-null float64\n",
+            "LotArea          1460 non-null int64\n",
+            "Street           1460 non-null object\n",
+            "Alley            91 non-null object\n",
+            "LotShape         1460 non-null object\n",
+            "LandContour      1460 non-null object\n",
+            "Utilities        1460 non-null object\n",
+            "LotConfig        1460 non-null object\n",
+            "LandSlope        1460 non-null object\n",
+            "Neighborhood     1460 non-null object\n",
+            "Condition1       1460 non-null object\n",
+            "Condition2       1460 non-null object\n",
+            "BldgType         1460 non-null object\n",
+            "HouseStyle       1460 non-null object\n",
+            "OverallQual      1460 non-null int64\n",
+            "OverallCond      1460 non-null int64\n",
+            "YearBuilt        1460 non-null int64\n",
+            "YearRemodAdd     1460 non-null int64\n",
+            "RoofStyle        1460 non-null object\n",
+            "RoofMatl         1460 non-null object\n",
+            "Exterior1st      1460 non-null object\n",
+            "Exterior2nd      1460 non-null object\n",
+            "MasVnrType       1452 non-null object\n",
+            "MasVnrArea       1452 non-null float64\n",
+            "ExterQual        1460 non-null object\n",
+            "ExterCond        1460 non-null object\n",
+            "Foundation       1460 non-null object\n",
+            "BsmtQual         1423 non-null object\n",
+            "BsmtCond         1423 non-null object\n",
+            "BsmtExposure     1422 non-null object\n",
+            "BsmtFinType1     1423 non-null object\n",
+            "BsmtFinSF1       1460 non-null int64\n",
+            "BsmtFinType2     1422 non-null object\n",
+            "BsmtFinSF2       1460 non-null int64\n",
+            "BsmtUnfSF        1460 non-null int64\n",
+            "TotalBsmtSF      1460 non-null int64\n",
+            "Heating          1460 non-null object\n",
+            "HeatingQC        1460 non-null object\n",
+            "CentralAir       1460 non-null object\n",
+            "Electrical       1459 non-null object\n",
+            "1stFlrSF         1460 non-null int64\n",
+            "2ndFlrSF         1460 non-null int64\n",
+            "LowQualFinSF     1460 non-null int64\n",
+            "GrLivArea        1460 non-null int64\n",
+            "BsmtFullBath     1460 non-null int64\n",
+            "BsmtHalfBath     1460 non-null int64\n",
+            "FullBath         1460 non-null int64\n",
+            "HalfBath         1460 non-null int64\n",
+            "BedroomAbvGr     1460 non-null int64\n",
+            "KitchenAbvGr     1460 non-null int64\n",
+            "KitchenQual      1460 non-null object\n",
+            "TotRmsAbvGrd     1460 non-null int64\n",
+            "Functional       1460 non-null object\n",
+            "Fireplaces       1460 non-null int64\n",
+            "FireplaceQu      770 non-null object\n",
+            "GarageType       1379 non-null object\n",
+            "GarageYrBlt      1379 non-null float64\n",
+            "GarageFinish     1379 non-null object\n",
+            "GarageCars       1460 non-null int64\n",
+            "GarageArea       1460 non-null int64\n",
+            "GarageQual       1379 non-null object\n",
+            "GarageCond       1379 non-null object\n",
+            "PavedDrive       1460 non-null object\n",
+            "WoodDeckSF       1460 non-null int64\n",
+            "OpenPorchSF      1460 non-null int64\n",
+            "EnclosedPorch    1460 non-null int64\n",
+            "3SsnPorch        1460 non-null int64\n",
+            "ScreenPorch      1460 non-null int64\n",
+            "PoolArea         1460 non-null int64\n",
+            "PoolQC           7 non-null object\n",
+            "Fence            281 non-null object\n",
+            "MiscFeature      54 non-null object\n",
+            "MiscVal          1460 non-null int64\n",
+            "MoSold           1460 non-null int64\n",
+            "YrSold           1460 non-null int64\n",
+            "SaleType         1460 non-null object\n",
+            "SaleCondition    1460 non-null object\n",
+            "SalePrice        1460 non-null int64\n",
+            "dtypes: float64(3), int64(35), object(43)\n",
+            "memory usage: 924.0+ KB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HxPPf4CPySYK",
+        "colab_type": "code",
+        "outputId": "63daec2c-cc09-44dc-bd13-6eff7d7727ce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 253
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>MSSubClass</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>LotFrontage</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>Street</th>\n",
+              "      <th>Alley</th>\n",
+              "      <th>LotShape</th>\n",
+              "      <th>LandContour</th>\n",
+              "      <th>Utilities</th>\n",
+              "      <th>LotConfig</th>\n",
+              "      <th>LandSlope</th>\n",
+              "      <th>Neighborhood</th>\n",
+              "      <th>Condition1</th>\n",
+              "      <th>Condition2</th>\n",
+              "      <th>BldgType</th>\n",
+              "      <th>HouseStyle</th>\n",
+              "      <th>OverallQual</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>YearRemodAdd</th>\n",
+              "      <th>RoofStyle</th>\n",
+              "      <th>RoofMatl</th>\n",
+              "      <th>Exterior1st</th>\n",
+              "      <th>Exterior2nd</th>\n",
+              "      <th>MasVnrType</th>\n",
+              "      <th>MasVnrArea</th>\n",
+              "      <th>ExterQual</th>\n",
+              "      <th>ExterCond</th>\n",
+              "      <th>Foundation</th>\n",
+              "      <th>BsmtQual</th>\n",
+              "      <th>BsmtCond</th>\n",
+              "      <th>BsmtExposure</th>\n",
+              "      <th>BsmtFinType1</th>\n",
+              "      <th>BsmtFinSF1</th>\n",
+              "      <th>BsmtFinType2</th>\n",
+              "      <th>BsmtFinSF2</th>\n",
+              "      <th>BsmtUnfSF</th>\n",
+              "      <th>TotalBsmtSF</th>\n",
+              "      <th>Heating</th>\n",
+              "      <th>...</th>\n",
+              "      <th>CentralAir</th>\n",
+              "      <th>Electrical</th>\n",
+              "      <th>1stFlrSF</th>\n",
+              "      <th>2ndFlrSF</th>\n",
+              "      <th>LowQualFinSF</th>\n",
+              "      <th>GrLivArea</th>\n",
+              "      <th>BsmtFullBath</th>\n",
+              "      <th>BsmtHalfBath</th>\n",
+              "      <th>FullBath</th>\n",
+              "      <th>HalfBath</th>\n",
+              "      <th>BedroomAbvGr</th>\n",
+              "      <th>KitchenAbvGr</th>\n",
+              "      <th>KitchenQual</th>\n",
+              "      <th>TotRmsAbvGrd</th>\n",
+              "      <th>Functional</th>\n",
+              "      <th>Fireplaces</th>\n",
+              "      <th>FireplaceQu</th>\n",
+              "      <th>GarageType</th>\n",
+              "      <th>GarageYrBlt</th>\n",
+              "      <th>GarageFinish</th>\n",
+              "      <th>GarageCars</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>GarageQual</th>\n",
+              "      <th>GarageCond</th>\n",
+              "      <th>PavedDrive</th>\n",
+              "      <th>WoodDeckSF</th>\n",
+              "      <th>OpenPorchSF</th>\n",
+              "      <th>EnclosedPorch</th>\n",
+              "      <th>3SsnPorch</th>\n",
+              "      <th>ScreenPorch</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>PoolQC</th>\n",
+              "      <th>Fence</th>\n",
+              "      <th>MiscFeature</th>\n",
+              "      <th>MiscVal</th>\n",
+              "      <th>MoSold</th>\n",
+              "      <th>YrSold</th>\n",
+              "      <th>SaleType</th>\n",
+              "      <th>SaleCondition</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>60</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>65.0</td>\n",
+              "      <td>8450</td>\n",
+              "      <td>Pave</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Reg</td>\n",
+              "      <td>Lvl</td>\n",
+              "      <td>AllPub</td>\n",
+              "      <td>Inside</td>\n",
+              "      <td>Gtl</td>\n",
+              "      <td>CollgCr</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>1Fam</td>\n",
+              "      <td>2Story</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>Gable</td>\n",
+              "      <td>CompShg</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>BrkFace</td>\n",
+              "      <td>196.0</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>PConc</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>No</td>\n",
+              "      <td>GLQ</td>\n",
+              "      <td>706</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>856</td>\n",
+              "      <td>GasA</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>SBrkr</td>\n",
+              "      <td>856</td>\n",
+              "      <td>854</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1710</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Typ</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Attchd</td>\n",
+              "      <td>2003.0</td>\n",
+              "      <td>RFn</td>\n",
+              "      <td>2</td>\n",
+              "      <td>548</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>0</td>\n",
+              "      <td>61</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>WD</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>20</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>80.0</td>\n",
+              "      <td>9600</td>\n",
+              "      <td>Pave</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Reg</td>\n",
+              "      <td>Lvl</td>\n",
+              "      <td>AllPub</td>\n",
+              "      <td>FR2</td>\n",
+              "      <td>Gtl</td>\n",
+              "      <td>Veenker</td>\n",
+              "      <td>Feedr</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>1Fam</td>\n",
+              "      <td>1Story</td>\n",
+              "      <td>6</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>Gable</td>\n",
+              "      <td>CompShg</td>\n",
+              "      <td>MetalSd</td>\n",
+              "      <td>MetalSd</td>\n",
+              "      <td>None</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>CBlock</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>ALQ</td>\n",
+              "      <td>978</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>0</td>\n",
+              "      <td>284</td>\n",
+              "      <td>1262</td>\n",
+              "      <td>GasA</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>SBrkr</td>\n",
+              "      <td>1262</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1262</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Typ</td>\n",
+              "      <td>1</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Attchd</td>\n",
+              "      <td>1976.0</td>\n",
+              "      <td>RFn</td>\n",
+              "      <td>2</td>\n",
+              "      <td>460</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>298</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2007</td>\n",
+              "      <td>WD</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>60</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>68.0</td>\n",
+              "      <td>11250</td>\n",
+              "      <td>Pave</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>IR1</td>\n",
+              "      <td>Lvl</td>\n",
+              "      <td>AllPub</td>\n",
+              "      <td>Inside</td>\n",
+              "      <td>Gtl</td>\n",
+              "      <td>CollgCr</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>1Fam</td>\n",
+              "      <td>2Story</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>Gable</td>\n",
+              "      <td>CompShg</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>BrkFace</td>\n",
+              "      <td>162.0</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>PConc</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Mn</td>\n",
+              "      <td>GLQ</td>\n",
+              "      <td>486</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>0</td>\n",
+              "      <td>434</td>\n",
+              "      <td>920</td>\n",
+              "      <td>GasA</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>SBrkr</td>\n",
+              "      <td>920</td>\n",
+              "      <td>866</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1786</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Typ</td>\n",
+              "      <td>1</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Attchd</td>\n",
+              "      <td>2001.0</td>\n",
+              "      <td>RFn</td>\n",
+              "      <td>2</td>\n",
+              "      <td>608</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>0</td>\n",
+              "      <td>42</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>WD</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>70</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>9550</td>\n",
+              "      <td>Pave</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>IR1</td>\n",
+              "      <td>Lvl</td>\n",
+              "      <td>AllPub</td>\n",
+              "      <td>Corner</td>\n",
+              "      <td>Gtl</td>\n",
+              "      <td>Crawfor</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>1Fam</td>\n",
+              "      <td>2Story</td>\n",
+              "      <td>7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>1970</td>\n",
+              "      <td>Gable</td>\n",
+              "      <td>CompShg</td>\n",
+              "      <td>Wd Sdng</td>\n",
+              "      <td>Wd Shng</td>\n",
+              "      <td>None</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>BrkTil</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>No</td>\n",
+              "      <td>ALQ</td>\n",
+              "      <td>216</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>0</td>\n",
+              "      <td>540</td>\n",
+              "      <td>756</td>\n",
+              "      <td>GasA</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>SBrkr</td>\n",
+              "      <td>961</td>\n",
+              "      <td>756</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1717</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Typ</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>Detchd</td>\n",
+              "      <td>1998.0</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>3</td>\n",
+              "      <td>642</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>272</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2006</td>\n",
+              "      <td>WD</td>\n",
+              "      <td>Abnorml</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>60</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>14260</td>\n",
+              "      <td>Pave</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>IR1</td>\n",
+              "      <td>Lvl</td>\n",
+              "      <td>AllPub</td>\n",
+              "      <td>FR2</td>\n",
+              "      <td>Gtl</td>\n",
+              "      <td>NoRidge</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>Norm</td>\n",
+              "      <td>1Fam</td>\n",
+              "      <td>2Story</td>\n",
+              "      <td>8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>Gable</td>\n",
+              "      <td>CompShg</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>VinylSd</td>\n",
+              "      <td>BrkFace</td>\n",
+              "      <td>350.0</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>PConc</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Av</td>\n",
+              "      <td>GLQ</td>\n",
+              "      <td>655</td>\n",
+              "      <td>Unf</td>\n",
+              "      <td>0</td>\n",
+              "      <td>490</td>\n",
+              "      <td>1145</td>\n",
+              "      <td>GasA</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>SBrkr</td>\n",
+              "      <td>1145</td>\n",
+              "      <td>1053</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2198</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Gd</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Typ</td>\n",
+              "      <td>1</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Attchd</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>RFn</td>\n",
+              "      <td>3</td>\n",
+              "      <td>836</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>TA</td>\n",
+              "      <td>Y</td>\n",
+              "      <td>192</td>\n",
+              "      <td>84</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>WD</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 81 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
+              "0   1          60       RL  ...        WD         Normal    208500\n",
+              "1   2          20       RL  ...        WD         Normal    181500\n",
+              "2   3          60       RL  ...        WD         Normal    223500\n",
+              "3   4          70       RL  ...        WD        Abnorml    140000\n",
+              "4   5          60       RL  ...        WD         Normal    250000\n",
+              "\n",
+              "[5 rows x 81 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TbazA1fRyFcB",
+        "colab_type": "text"
+      },
+      "source": [
+        "Selecionando apenas as features numericas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BZt4EL9_yFmM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = df.select_dtypes(include=['int64', 'float64'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-tG-F_aQyFtX",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0vDXGj1IyF3Z",
+        "colab_type": "code",
+        "outputId": "54e8cf94-1629-434b-b240-445a0a8cccb8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 317
+        }
+      },
+      "source": [
+        "df.describe()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Id</th>\n",
+              "      <th>MSSubClass</th>\n",
+              "      <th>LotFrontage</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>OverallQual</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>YearRemodAdd</th>\n",
+              "      <th>MasVnrArea</th>\n",
+              "      <th>BsmtFinSF1</th>\n",
+              "      <th>BsmtFinSF2</th>\n",
+              "      <th>BsmtUnfSF</th>\n",
+              "      <th>TotalBsmtSF</th>\n",
+              "      <th>1stFlrSF</th>\n",
+              "      <th>2ndFlrSF</th>\n",
+              "      <th>LowQualFinSF</th>\n",
+              "      <th>GrLivArea</th>\n",
+              "      <th>BsmtFullBath</th>\n",
+              "      <th>BsmtHalfBath</th>\n",
+              "      <th>FullBath</th>\n",
+              "      <th>HalfBath</th>\n",
+              "      <th>BedroomAbvGr</th>\n",
+              "      <th>KitchenAbvGr</th>\n",
+              "      <th>TotRmsAbvGrd</th>\n",
+              "      <th>Fireplaces</th>\n",
+              "      <th>GarageYrBlt</th>\n",
+              "      <th>GarageCars</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>WoodDeckSF</th>\n",
+              "      <th>OpenPorchSF</th>\n",
+              "      <th>EnclosedPorch</th>\n",
+              "      <th>3SsnPorch</th>\n",
+              "      <th>ScreenPorch</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>MiscVal</th>\n",
+              "      <th>MoSold</th>\n",
+              "      <th>YrSold</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1201.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1452.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1379.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>1460.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>730.500000</td>\n",
+              "      <td>56.897260</td>\n",
+              "      <td>70.049958</td>\n",
+              "      <td>10516.828082</td>\n",
+              "      <td>6.099315</td>\n",
+              "      <td>5.575342</td>\n",
+              "      <td>1971.267808</td>\n",
+              "      <td>1984.865753</td>\n",
+              "      <td>103.685262</td>\n",
+              "      <td>443.639726</td>\n",
+              "      <td>46.549315</td>\n",
+              "      <td>567.240411</td>\n",
+              "      <td>1057.429452</td>\n",
+              "      <td>1162.626712</td>\n",
+              "      <td>346.992466</td>\n",
+              "      <td>5.844521</td>\n",
+              "      <td>1515.463699</td>\n",
+              "      <td>0.425342</td>\n",
+              "      <td>0.057534</td>\n",
+              "      <td>1.565068</td>\n",
+              "      <td>0.382877</td>\n",
+              "      <td>2.866438</td>\n",
+              "      <td>1.046575</td>\n",
+              "      <td>6.517808</td>\n",
+              "      <td>0.613014</td>\n",
+              "      <td>1978.506164</td>\n",
+              "      <td>1.767123</td>\n",
+              "      <td>472.980137</td>\n",
+              "      <td>94.244521</td>\n",
+              "      <td>46.660274</td>\n",
+              "      <td>21.954110</td>\n",
+              "      <td>3.409589</td>\n",
+              "      <td>15.060959</td>\n",
+              "      <td>2.758904</td>\n",
+              "      <td>43.489041</td>\n",
+              "      <td>6.321918</td>\n",
+              "      <td>2007.815753</td>\n",
+              "      <td>180921.195890</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>421.610009</td>\n",
+              "      <td>42.300571</td>\n",
+              "      <td>24.284752</td>\n",
+              "      <td>9981.264932</td>\n",
+              "      <td>1.382997</td>\n",
+              "      <td>1.112799</td>\n",
+              "      <td>30.202904</td>\n",
+              "      <td>20.645407</td>\n",
+              "      <td>181.066207</td>\n",
+              "      <td>456.098091</td>\n",
+              "      <td>161.319273</td>\n",
+              "      <td>441.866955</td>\n",
+              "      <td>438.705324</td>\n",
+              "      <td>386.587738</td>\n",
+              "      <td>436.528436</td>\n",
+              "      <td>48.623081</td>\n",
+              "      <td>525.480383</td>\n",
+              "      <td>0.518911</td>\n",
+              "      <td>0.238753</td>\n",
+              "      <td>0.550916</td>\n",
+              "      <td>0.502885</td>\n",
+              "      <td>0.815778</td>\n",
+              "      <td>0.220338</td>\n",
+              "      <td>1.625393</td>\n",
+              "      <td>0.644666</td>\n",
+              "      <td>24.689725</td>\n",
+              "      <td>0.747315</td>\n",
+              "      <td>213.804841</td>\n",
+              "      <td>125.338794</td>\n",
+              "      <td>66.256028</td>\n",
+              "      <td>61.119149</td>\n",
+              "      <td>29.317331</td>\n",
+              "      <td>55.757415</td>\n",
+              "      <td>40.177307</td>\n",
+              "      <td>496.123024</td>\n",
+              "      <td>2.703626</td>\n",
+              "      <td>1.328095</td>\n",
+              "      <td>79442.502883</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>1300.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1872.000000</td>\n",
+              "      <td>1950.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>334.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>334.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1900.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2006.000000</td>\n",
+              "      <td>34900.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>365.750000</td>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>7553.500000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1954.000000</td>\n",
+              "      <td>1967.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>223.000000</td>\n",
+              "      <td>795.750000</td>\n",
+              "      <td>882.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1129.500000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1961.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>334.500000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>2007.000000</td>\n",
+              "      <td>129975.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>730.500000</td>\n",
+              "      <td>50.000000</td>\n",
+              "      <td>69.000000</td>\n",
+              "      <td>9478.500000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1973.000000</td>\n",
+              "      <td>1994.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>383.500000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>477.500000</td>\n",
+              "      <td>991.500000</td>\n",
+              "      <td>1087.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1464.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1980.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>480.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>25.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>2008.000000</td>\n",
+              "      <td>163000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1095.250000</td>\n",
+              "      <td>70.000000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>11601.500000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>6.000000</td>\n",
+              "      <td>2000.000000</td>\n",
+              "      <td>2004.000000</td>\n",
+              "      <td>166.000000</td>\n",
+              "      <td>712.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>808.000000</td>\n",
+              "      <td>1298.250000</td>\n",
+              "      <td>1391.250000</td>\n",
+              "      <td>728.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1776.750000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>576.000000</td>\n",
+              "      <td>168.000000</td>\n",
+              "      <td>68.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>214000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1460.000000</td>\n",
+              "      <td>190.000000</td>\n",
+              "      <td>313.000000</td>\n",
+              "      <td>215245.000000</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>1600.000000</td>\n",
+              "      <td>5644.000000</td>\n",
+              "      <td>1474.000000</td>\n",
+              "      <td>2336.000000</td>\n",
+              "      <td>6110.000000</td>\n",
+              "      <td>4692.000000</td>\n",
+              "      <td>2065.000000</td>\n",
+              "      <td>572.000000</td>\n",
+              "      <td>5642.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>1418.000000</td>\n",
+              "      <td>857.000000</td>\n",
+              "      <td>547.000000</td>\n",
+              "      <td>552.000000</td>\n",
+              "      <td>508.000000</td>\n",
+              "      <td>480.000000</td>\n",
+              "      <td>738.000000</td>\n",
+              "      <td>15500.000000</td>\n",
+              "      <td>12.000000</td>\n",
+              "      <td>2010.000000</td>\n",
+              "      <td>755000.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                Id   MSSubClass  ...       YrSold      SalePrice\n",
+              "count  1460.000000  1460.000000  ...  1460.000000    1460.000000\n",
+              "mean    730.500000    56.897260  ...  2007.815753  180921.195890\n",
+              "std     421.610009    42.300571  ...     1.328095   79442.502883\n",
+              "min       1.000000    20.000000  ...  2006.000000   34900.000000\n",
+              "25%     365.750000    20.000000  ...  2007.000000  129975.000000\n",
+              "50%     730.500000    50.000000  ...  2008.000000  163000.000000\n",
+              "75%    1095.250000    70.000000  ...  2009.000000  214000.000000\n",
+              "max    1460.000000   190.000000  ...  2010.000000  755000.000000\n",
+              "\n",
+              "[8 rows x 38 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mEfXmKH1yGCr",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "U0KsZkMZyGnW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = df.fillna(df.median())\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TNhEpIuJyG0C",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis indepedentes e dependentes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dB00tGUXyG84",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X = df.iloc[:, :37].values\n",
+        "y = df.iloc[:, -1].values.reshape(-1,1)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GHvWZhwryHjv",
+        "colab_type": "text"
+      },
+      "source": [
+        "Dividindo o dataset em conjunto de treinamento e testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Li7E8Hz4yHuq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hIH9DERgyHH3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Criando o dicionário contendo todos os regressores"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eNFfJ7XKyGU1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "regressors = {'Linear Regression': LinearRegression(),\n",
+        "              'Decision Tree Reg:': DecisionTreeRegressor(random_state = 0),\n",
+        "              'Random Forest Reg': RandomForestRegressor(n_estimators = 10, random_state = 0),\n",
+        "              'SVR:': SVR(kernel = 'rbf')}"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y2v9PGGqyhYX",
+        "colab_type": "text"
+      },
+      "source": [
+        "Criando dataframe que irá guardar os resultados finais dos regressores"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Dh5XDN5eyhgs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df_results = pd.DataFrame(columns=['reg', 'rmse', 'rmse_log', 'r_2_score'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JliIuH-NylaA",
+        "colab_type": "text"
+      },
+      "source": [
+        "Percorrendo o dicionário e treinando e avaliando os modelos:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1dRjpg9Mylkg",
+        "colab_type": "code",
+        "outputId": "4ce690cd-27c7-472b-9dc7-039d9149fec8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 139
+        }
+      },
+      "source": [
+        "for name, reg in regressors.items():\n",
+        "    \n",
+        "    # Treinando os regressores com Conjunto de Treinamento\n",
+        "    reg.fit(X_train, y_train)\n",
+        "    \n",
+        "    # Prevendo os resultados com o conjunto de testes\n",
+        "    y_pred = reg.predict(X_test)\n",
+        "    \n",
+        "    df_results.loc[len(df_results), :] = [name, reg.score(X_test, y_test), \n",
+        "                   mean_squared_error(y_test, y_pred), mean_squared_log_error(y_test, y_pred)]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+            "  after removing the cwd from sys.path.\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+            "  y = column_or_1d(y, warn=True)\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+            "  \"avoid this warning.\", FutureWarning)\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jH-rHO9Cy0je",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibindo os resultados:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3kYKXzOHy0uG",
+        "colab_type": "code",
+        "outputId": "413713e5-0553-4a11-9ba5-63156b66b564",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        }
+      },
+      "source": [
+        "df_results"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>reg</th>\n",
+              "      <th>rmse</th>\n",
+              "      <th>rmse_log</th>\n",
+              "      <th>r_2_score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Linear Regression</td>\n",
+              "      <td>0.822678</td>\n",
+              "      <td>1.36012e+09</td>\n",
+              "      <td>0.0358379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Decision Tree Reg:</td>\n",
+              "      <td>0.790632</td>\n",
+              "      <td>1.60592e+09</td>\n",
+              "      <td>0.0453292</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Random Forest Reg</td>\n",
+              "      <td>0.879513</td>\n",
+              "      <td>9.24177e+08</td>\n",
+              "      <td>0.0289542</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>SVR:</td>\n",
+              "      <td>-0.0249732</td>\n",
+              "      <td>7.86188e+09</td>\n",
+              "      <td>0.186869</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                  reg       rmse     rmse_log  r_2_score\n",
+              "0   Linear Regression   0.822678  1.36012e+09  0.0358379\n",
+              "1  Decision Tree Reg:   0.790632  1.60592e+09  0.0453292\n",
+              "2   Random Forest Reg   0.879513  9.24177e+08  0.0289542\n",
+              "3                SVR: -0.0249732  7.86188e+09   0.186869"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 65
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From ed748e1fbfa3c51210450bb51877bb235f60d70b Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Tue, 12 Nov 2019 16:32:40 -0300
Subject: [PATCH 02/14] Delete 3_regression.ipynb

---
 notebooks/3_regression.ipynb | 3981 ----------------------------------
 1 file changed, 3981 deletions(-)
 delete mode 100644 notebooks/3_regression.ipynb

diff --git a/notebooks/3_regression.ipynb b/notebooks/3_regression.ipynb
deleted file mode 100644
index 8921f6a..0000000
--- a/notebooks/3_regression.ipynb
+++ /dev/null
@@ -1,3981 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "3_regression.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "TsV0SiuwpZ7i",
-        "7cfguFoQp2_-",
-        "hDpjk2hPqBar",
-        "fB8cnF96qcQM",
-        "MyrmVTcFtw9L",
-        "Ood5PUZCtxsa",
-        "VUZAwJjMvh0r",
-        "KDcimzDmxxIo"
-      ],
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/3_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "#**Semana 3 - Modelos de Regressão**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TsV0SiuwpZ7i",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs e funções:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regressão\n",
-        "def plot_results_linear(X, y, regressor, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
-        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()    \n",
-        "    \n",
-        "# Função que gera o gráfico dos resultados de arvores\n",
-        "def plot_results_reg(X, y, regressor, title):     \n",
-        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
-        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "     "
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QaGBc_KwpeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos **dados**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/r4phael/ml-course/master/data/pricing_houses.csv')\n",
-        "\n",
-        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "cc0eccc5-6bb5-4ead-a7b9-7596700a9280",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "df.describe()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.0</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>11633.933333</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>504.683333</td>\n",
-              "      <td>5.783333</td>\n",
-              "      <td>1972.116667</td>\n",
-              "      <td>197536.150000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>8099.879028</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>222.427554</td>\n",
-              "      <td>1.180228</td>\n",
-              "      <td>30.522618</td>\n",
-              "      <td>74910.507145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>8068.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.750000</td>\n",
-              "      <td>142250.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>10510.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>542.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.500000</td>\n",
-              "      <td>188750.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>12010.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>607.250000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>53227.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
-              "count     60.000000      60.0  ...    60.000000      60.000000\n",
-              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
-              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
-              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
-              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
-              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
-              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
-              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 6 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "7ef0eb04-db39-4e20-bcd3-a2c266e5e15a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>789</th>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1039</th>\n",
-              "      <td>1477</td>\n",
-              "      <td>0</td>\n",
-              "      <td>286</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1970</td>\n",
-              "      <td>RM</td>\n",
-              "      <td>80000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>868</th>\n",
-              "      <td>14762</td>\n",
-              "      <td>0</td>\n",
-              "      <td>672</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1948</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>169000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>782</th>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>611</th>\n",
-              "      <td>10395</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>148000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "789     12205         0         444            8       1966       RL     187500\n",
-              "1039     1477         0         286            4       1970       RM      80000\n",
-              "868     14762         0         672            6       1948       RL     169000\n",
-              "782     16285         0         605            5       2001       RL     187100\n",
-              "611     10395         0         564            6       1978       RL     148000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "ee2fbfca-21aa-4e0d-8410-502c6c747df6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>789</th>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1039</th>\n",
-              "      <td>1477</td>\n",
-              "      <td>0</td>\n",
-              "      <td>286</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1970</td>\n",
-              "      <td>RM</td>\n",
-              "      <td>80000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>868</th>\n",
-              "      <td>14762</td>\n",
-              "      <td>0</td>\n",
-              "      <td>672</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1948</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>169000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>782</th>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>611</th>\n",
-              "      <td>10395</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>148000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "789     12205         0         444            8       1966       RL     187500\n",
-              "1039     1477         0         286            4       1970       RM      80000\n",
-              "868     14762         0         672            6       1948       RL     169000\n",
-              "782     16285         0         605            5       2001       RL     187100\n",
-              "611     10395         0         564            6       1978       RL     148000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "78f424a9-14e0-42ae-c0b1-5a9d20472a25",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[12205],\n",
-              "       [ 1477],\n",
-              "       [14762],\n",
-              "       [16285],\n",
-              "       [10395]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "fe5cbf44-fe44-4b28-9baf-a50ecf037b33",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[187500],\n",
-              "       [ 80000],\n",
-              "       [169000],\n",
-              "       [187100],\n",
-              "       [148000]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yzJ08rJ4J3CS",
-        "colab_type": "text"
-      },
-      "source": [
-        " Criando os subconjuntos de treinamento e testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tVz1mTUOJ4Tb",
-        "colab_type": "code",
-        "outputId": "4f5b4396-f9c4-4d11-c71d-7227ea74ee4c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 68
-        }
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
-        "\n",
-        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
-        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
-        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Tamanho do Dataset. 60\n",
-            "Tamanho do Conjunto de Treinamento. 48\n",
-            "Tamanho do Conjunto de Testes. 12\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "#X_train = feature_scaling(X_train)\n",
-        "#X_test = feature_scaling(X_test)\n",
-        "\n",
-        "#X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7cfguFoQp2_-",
-        "colab_type": "text"
-      },
-      "source": [
-        "###  Modelo - Regressão Linear"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jGXuG35WJ_Tj",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "xo9Q5lxsKAVB",
-        "colab_type": "code",
-        "outputId": "d60bc5b4-d05e-4e4e-da6b-e51867d2076c",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "\n",
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "FUYWJqnaKBvh",
-        "colab_type": "text"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SFvsig4SKCiC",
-        "colab_type": "code",
-        "outputId": "a8b625a1-50b2-4b45-f4cf-c02a757b93ea",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 221
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[207117.08544721],\n",
-              "       [183686.58545274],\n",
-              "       [169299.32577351],\n",
-              "       [179936.94922447],\n",
-              "       [188476.03676953],\n",
-              "       [191960.99279345],\n",
-              "       [202201.59593788],\n",
-              "       [465634.02218018],\n",
-              "       [206688.55559255],\n",
-              "       [176212.52063471],\n",
-              "       [195653.91183507],\n",
-              "       [185759.91372014]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kRjvN45RKFPq",
-        "colab_type": "text"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r²"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "I3_jQ8N5KH-6",
-        "colab_type": "code",
-        "outputId": "b6945795-2198-4faf-9dab-41d33ef3fa65",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "-0.9506477517227611"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "rTahZRcqKS2b",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "2EAqhxovKT9a",
-        "colab_type": "code",
-        "outputId": "64515476-b062-45cf-94f4-4c2933868406",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_linear(X_train, y_train, regressor, 'Regressão Linear (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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Lc86VsVWrQn+p228Pa9eG2J13hqRy/PHFK5cnF+ecK0MffQT77AO9e8PChSF27bUhqYwZ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\n55zLO08uzjnn8u7/AwrycyiLfw2dAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hwRwnq82KU2w",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "uXKEZrWQKV9R",
-        "colab_type": "code",
-        "outputId": "6ac6a682-61d0-4171-852e-dfc66cf6fa0e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_linear(X_test, y_test, regressor, 'Regressão Linear (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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/+UtKKrvsUrtymXUlbSaX3Jz4E5JuJlWkHwCcSmOHyrZsSXro5SxSIpgaEbcC\npwDflNRAqh+5Mm9/JbBZjn8zn4dc13MjaQTMO4Cv5tttK4ETSB085wE3FuqFWjuHWYctXQqDBsHg\nwY2xm29OSWW//WpXLrOuSOmLfgsrpF8CBwF/IbXE+kMelbJHqquri/r6+loXw7qg119PyaP453HZ\nZXDccbUrk1lXIWlGRNQ1j7fVWuwO4LiIeKV6xTLrulauhI9/HCZPboyddRaceWbNimTWbbSaXCLi\n2s4siFlXEQFf/SpcemljbPz4dLUi1a5cZt1JOeO5mPUa55wDZ5zRuHzYYfDb36bHt5hZ+fwvYwZc\ncQV86UuNyyNHwt13v/2pO2ZWnjUmF0n9gZOBIRHxJUnDge1zyy+zbu2WW+CIwsOB3vUumDsXNt20\ndmUy6wnK6efyC9JDKEtdxRYC51StRGad4J57Uv1JMbE880waGdKJxWztlZNcto2IHwNvAUTEa4Cr\nNa1bmjMnJZUPfKAxNnduqsTfeuvW9zOz9iknuazIPexL47lsS7qSMes2nnkmJZWdd26M/e1vKakU\ne9qbWWWUk1zOJPV52VrSROBO4NtVLZVZhbzwQnp45NChjbHJk1NS2Wef2pXLrKdbY4V+REyV9A9g\nFOl22IkR8a+ql8xsLbz6akoeswojD115JXzhC7Urk1lv0mpykTSyWWhR/jlE0pA8iJhZl/LWW/DR\nj8IddzTGzj03jVdvZp2nrSuXn+Sf6wF1wEOkK5ddgHoaW4+Z1VwEfPGLcNVVjbGvfS0NMexe9Wad\nr63xXD6Ux3JZBIzMg2rtDuyGx0exLuS7301j0pcSy3/9V3ou2MUXO7GY1Uo5PfS3j4jZpYWImCNp\nhyqWyawsP/tZegZYyahRMG0arLde7cpkZkk5rcVmSbpC0v55uhyYtca9zKrkppvSFUkpsQwZksZa\nufdeJxazdpk4MQ2b+o53pJ8TJ1bs0OVcuRwDfBk4MS//lTQipVmn+vOf4UMfalxeZx14+mnYqpyh\n68ysqYkT0+O+X3stLT/9dFoGGDt2rQ/f6mBhvY0HC+u6Zs6E3XZrGnvkEdh++9qUx6xHGDYsJZTm\nhg6F+fPLPkxHBgszq6mnnoJ3v7tp7L77YM89a1Mesx7lmWfaF2+ncupcOkTS1pKmSXpY0lxJJ+b4\nWZIWSpqZp0MK+5wmqUHSo5IOLsTH5FiDpFML8W0k3ZfjN0haN8f75eWGvH5YtV6nVd7ixbDxxk0T\ny+23p+bGTixmFTJkSPvi7VR2cpG0oaQN23HslcDJETGC1Lv/q5JKT3G6ICJ2zdNt+fgjgKOAHYEx\nwM8k9ZHUB7gE+AgwAvh04Rw0rMIAABajSURBVDg/ysfaDlgKHJvjxwJLc/yCvJ11ccuXww47wBZb\nwCt5cO3rrktJZcyY2pbNrMf5wQ/ePmBR//4pXgFrTC6Sdpb0IDAXeFjSDEk7rWm/iFhU6sUfEa8A\n84BBbexyODApIt6MiKeABmDPPDVExJMRsQKYBBwuScABwE15/2uAIwrHuibP3wQcmLe3LmjFCjjw\nQNhoo1SXAnD++SmpfPaztS2bWY81dixMmJDqWKT0c8KEilTmQ3lXLj8HvhkRQyNiCGngsAntOUm+\nLbUbcF8OnSBplqSrJG2SY4OAZwu7Lcix1uKbAS9FxMpm8SbHyuuX5e2bl2u8pHpJ9UuWLGnPS7IK\nWL0aPvc56NcP7rorxU4+OcVPPrm2ZTPrFcaOTZX3q1ennxVKLFBectkgIqaVFiLiz8AG5Z4g30r7\nDXBSRLxMasa8LbArqff/T9rYvaoiYkJ+8kDdwIEDa1WMXicCTjkF+vSB669PsU9/GlatSlcsvsY0\n6/7KaS32pKQzgOvy8meBJ8s5uKS+pMQyMSJuBoiI5wvrLwdKwyUvBIrDNQ2m8TEzLcVfAAZIWidf\nnRS3Lx1rgaR1gHfm7a3GLroITjqpcXm//eCPf0xXL2bWc5Rz5fIFYCBwMylRbJ5jbcp1HFcC8yLi\nfwvxLQubfQyYk+cnA0flll7bAMOB+4EHgOG5Zdi6pEr/yZE66EwDjsz7jwNuKRxrXJ4/Ergr3KGn\npn71q3RFUkos220Hy5bBX/7ixGLWE5Vz5bKcNIZLaSRKlbnfvsDngNmSZubYd0itvXYljWw5HzgO\nICLmSroReJjU0uyrEbEqn/MEYArQB7gqIubm450CTJJ0DvAgKZmRf14nqQF4kZSQrAamToXRoxuX\n+/eHJ59MLcLMrOdaYw99SfcCo3OLLyRtDNwOHA30jYhHql7KTuAe+pU1YwbUNeuz29AA225bm/KY\nWXW01kO/nNti65cSC0CulB8GfBz4fcVKaD3C44+n21/FxDJjRqrEd2Ix6z3KSS6vSnpfaUHSbsCz\nEfFjGus4rJd77rn0ROL3vKcxNnVqSiojm49pamY9Xjl1JycBv5H0T9JIlO8i12FExLeqWDbrBpYt\nSw+VfOqpxtikSfCpT9WuTGZWe2tMLhHxQB4crPQM2kcj4q3qFsu6ujffhIMOgnvuaYxddBF8/eu1\nK5OZdR3lPP6lP6lV1okRMQcYJumwqpfM1k6VBgFatSpdlay3XmNiOe20dPvLicXMSsqpc/kFsALY\nOy8vBM6pWols7ZUGAXr66fSpXxoEaC0STAR84xtpgK4bb0yxceNSsjn33AqV28x6jHKSy7a58v4t\ngIh4jVT3Yl3V6ac3ji5X8tprKd4BP/5xugC68MK0/OEPp9tiV1+d4mZmzZVTob9C0vqkTo9I2hZ4\ns6qlsrVToUGArr02XZ2U7LgjTJ8OG7Zn4AUz65XKSS5nAncAW0uaSOp5//lqFsrW0pAhLQ9fWuYg\nQLfdBoce2rg8YAA89hj42Z5mVq42b2rkR708AvwXKaH8CqjLT0a2rqqDgwBNn546QBYTy1NPwdKl\nTixm1j5tXrlEREi6LSJ2Bv7QSWWytVUak+H009OtsCFDUmJpZayGRx5JI0AWPfQQ7LJLlctpZj1W\nObfF/iFpj4h4oOqlscoZO3aNA/8sXAiDBzeN/fnP8MEPVq9YZtY7lNPWZy9guqQn8uiRsyXNqnbB\nrHqWLoVBg5omlptuSs2NnVjMrBLKuXI5uOqlsE7x+utpcK7iw58vvRSOP752ZTKznqnV5CJpPeB4\nYDtgNnBlYbx660ZWroQjj4RbCo8ZPfNMOOusmhXJzHq4tq5criF1nLwb+AgwAjixMwpllRGR+qlc\nd11jbPx4uOwyj1NvZtXVVnIZkVuJIelK0pDD1k2MHp0eeV9y6KHwu9+lx7eYmVVbWxX6/37ycUdu\nh0naWtI0SQ9LmivpxBzfVNJUSY/nn5vkuCRdLKkhNxwYWTjWuLz945LGFeK75wYGDXlftXWO3uAr\nX0lXJcXE8uqrcOutTixm1nnaSi7vk/Rynl4BdinNS3q5jGOvBE6OiBHAKOCrkkYApwJ3RsRw4M68\nDOnW2/A8jQcuhZQoSE8J2AvYEzizkCwuBb5U2G9Mjrd2js5TpacSt+a881JSufTSxtiCBenWWPP+\nlGZm1dZqcomIPhGxcZ42ioh1CvMbr+nAEbEoIv6R518B5gGDgMNJ9Tnkn0fk+cOBayOZDgyQtCWp\ntdrUiHgxIpYCU4Exed3GETE9IgK4ttmxWjpH56jCU4lb88tfpqTy7W83xmbPTqcdNKjipzMzK0un\nPNNW0jBgN+A+YIuIWJRXPQdskecHAc8WdluQY23FF7QQp41zdI4KP5W4JdOmpaRS7Cc5bVpKKjvt\nVLHTmJl1SNWTi6QNgd8AJ0VEk9tp+Yojqnn+ts4habykekn1S5YsqdxJK/RU4pbMnp2SygEHNMYm\nTUpJZf/91/rwZmYVUdXkIqkvKbFMjIibc/j5fEuL/HNxji8Eti7sPjjH2ooPbiHe1jmaiIgJEVEX\nEXUDK/lkxtaePlzmU4lb8tBDKakUn/d1/vkpqXi8ejPraqqWXHLLrSuBeRHxv4VVk4FSi69xwC2F\n+NG51dgoYFm+tTUFGC1pk1yRPxqYkte9LGlUPtfRzY7V0jk6RzlPJS6zwv+f/0xJZdddG2MnnJCS\nysknV7zkZmaVERFVmYD3k25HzQJm5ukQYDNSC67HgT8Bm+btBVwCPEF6IkBd4VhfABrydEwhXgfM\nyfv8H6Acb/EcbU277757VNT110cMHRohpZ/XX990Xf/+ESlHpKl//ybbLF/edHVpqsj5zcwqBKiP\nFj5TSx/GvV5dXV3UFx+6VU3DhrU8mNfQoax6Yn6L/VFWr25Hr/pSa7Vio4L+/WHChDU+KdnMrD0k\nzYiIuuZxj4BeC61U7OvptyeWN95I1yztelxLJ7RWMzNri5NLLTSr2BeBmjVoW7w4JZV+/Tpw/Cq2\nVjMzK4eTSy3kCv9P88u3JZV581JSWavGa1VorWZm1h5OLjVw1uNj0WuvMolP/zt213f+RAS8970V\nOEE5rdXMzKrIjzLsRNdemx6BX3TLLfDRjwIcVLkTlSrtTz893QobMiQlFlfmm1kncXLpBHfdBQce\n2DT205+m/ipVM3ask4mZ1YyTSxU98wwMHdo0duKJcOGFtSmPmVlncZ1LFSxfDjvs0DSxHHxwqqhv\nM7F08mP6zcyqxcmlglasgIMOgo02gkceSbEvfSkllTvuWMPOnfiYfjOzanNyqYDVq+Hoo1OflDvv\nTLGTT07xCRPKPIg7PppZD+I6l7U0cybstlvj8mc+A9ddl+5stYs7PppZD+Irl7X04IPp5/77p0e1\nTJzYgcQC7vhoZj2Kk8taOuaYVEUybVoHH9VS4o6PZtaDOLl0FWPHpgqaoUPTUyqHDvVTjM2s23Kd\nS1fijo9m1kP4ysXMzCrOyaXW3HHSzHog3xarpeYjRpY6ToJvj5lZt1a1KxdJV0laLGlOIXaWpIWS\nZubpkMK60yQ1SHpU0sGF+Jgca5B0aiG+jaT7cvwGSevmeL+83JDXD6vWa1xr7jhpZj1UNW+LXQ2M\naSF+QUTsmqfbACSNAI4Cdsz7/ExSH0l9gEuAjwAjgE/nbQF+lI+1HbAUODbHjwWW5vgFebuuqa2O\nk75dZmbdWNWSS0T8FXixzM0PByZFxJsR8RTQAOyZp4aIeDIiVgCTgMMlCTgAuCnvfw1wROFY1+T5\nm4AD8/ZdT2sdJDfd1M8ZM7NurRYV+idImpVvm22SY4OAZwvbLMix1uKbAS9FxMpm8SbHyuuX5e27\nntY6ToJvl5lZt9bZyeVSYFtgV2AR8JNOPn8TksZLqpdUv2TJks4vQGsdJ19s5YLPzxkzs26iU5NL\nRDwfEasiYjVwOem2F8BCYOvCpoNzrLX4C8AASes0izc5Vl7/zrx9S+WZEBF1EVE3cODAtX15HTN2\nLMyfnx6hPH9+WvZzxsysm+vU5CJpy8Lix4BSS7LJwFG5pdc2wHDgfuABYHhuGbYuqdJ/ckQEMA04\nMu8/DrilcKzSSPVHAnfl7bsPP2fMzLq5qvVzkfQrYH9gc0kLgDOB/SXtCgQwHzgOICLmSroReBhY\nCXw1Ilbl45wATAH6AFdFxNx8ilOASZLOAR4ErszxK4HrJDWQGhQcVa3XWDWlPi6nn55uhQ0ZkhKL\n+76YWTeh7valvlrq6uqivr6+1sXomIkTnYjMrCYkzYiIuuZx99Dv7tzL38y6ID9brLtzL38z64Kc\nXLo7D49sZl2Qk0t352bLZtYFObl0d262bGZdkJNLd+fhkc2sC3JrsZ7AwyObWRfjKxczM6s4Jxcz\nM6s4J5e14QG9zMxa5DqXjnLPeDOzVvnKpaPcM97MrFVOLh3lnvFmZq1ycuko94w3M2uVk0tHuWe8\nmVmrnFw6yj3jzcxa5dZia8M9483MWuQrFzMzq7iqJRdJV0laLGlOIbappKmSHs8/N8lxSbpYUoOk\nWZJGFvYZl7d/XNK4Qnx3SbPzPhdLUlvnMDOzzlPNK5ergTHNYqcCd0bEcODOvAzwEWB4nsYDl0JK\nFMCZwF7AnsCZhWRxKfClwn5j1nCO2nJvfjPrRaqWXCLir8CLzcKHA9fk+WuAIwrxayOZDgyQtCVw\nMDA1Il6MiKXAVGBMXrdxREyPiACubXasls5RO6Xe/E8/DRGNvfmdYMysh+rsOpctImJRnn8O2CLP\nDwKeLWy3IMfaii9oId7WOWrHvfnNrJepWYV+vuKIWp5D0nhJ9ZLqlyxZUr2CuDe/mfUynZ1cns+3\ntMg/F+f4QmDrwnaDc6yt+OAW4m2d420iYkJE1EVE3cCBAzv8otbIvfnNrJfp7OQyGSi1+BoH3FKI\nH51bjY0CluVbW1OA0ZI2yRX5o4Eped3LkkblVmJHNztWS+eoHffmN7NepppNkX8F3AtsL2mBpGOB\nHwIflvQ4cFBeBrgNeBJoAC4HvgIQES8CZwMP5On7OUbe5oq8zxPA7Tne2jlqx735zayXUaqWsLq6\nuqivr691MczMuhVJMyKirnncPfTNzKzinFzMzKzinFzMzKzinFzMzKzinFzMzKzi3Fosk7QEeLrW\n5WjD5sC/al2IGurtrx/8HoDfA+h678HQiHhbL3Qnl25CUn1Lzf16i97++sHvAfg9gO7zHvi2mJmZ\nVZyTi5mZVZyTS/cxodYFqLHe/vrB7wH4PYBu8h64zsXMzCrOVy5mZlZxTi5mZlZxTi6dSNJVkhZL\nmlOIbSppqqTH889NclySLpbUIGmWpJGFfcbl7R+XNK4Q313S7LzPxXmsmy5F0taSpkl6WNJcSSfm\neK94HyStJ+l+SQ/l1/+9HN9G0n25zDdIWjfH++Xlhrx+WOFYp+X4o5IOLsTH5FiDpFM7+zWWS1If\nSQ9KujUv96r3QNL8/Hc6U1J9jvWc/4OI8NRJE7AfMBKYU4j9GDg1z58K/CjPH0Iao0bAKOC+HN+U\nNPbNpsAmeX6TvO7+vK3yvh+p9Wtu4T3YEhiZ5zcCHgNG9Jb3IZdpwzzfF7gvl/VG4Kgcvwz4cp7/\nCnBZnj8KuCHPjwAeAvoB25DGNOqTpyeAdwPr5m1G1Pp1t/JefBP4JXBrXu5V7wEwH9i8WazH/B/4\nyqUTRcRfgRebhQ8Hrsnz1wBHFOLXRjIdGKA0bPPBwNSIeDEilgJTgTF53cYRMT3SX9a1hWN1GRGx\nKCL+kedfAeYBg+gl70N+HcvzYt88BXAAcFOON3/9pfflJuDA/A30cGBSRLwZEU+RBs3bM08NEfFk\nRKwAJuVtuxRJg4FDSQP+kV9Tr3oPWtFj/g+cXGpvi0jDNgM8B2yR5wcBzxa2W5BjbcUXtBDvsvLt\njd1I3957zfuQbwfNBBaTPgyeAF6KiJV5k2KZ//068/plwGa0/33pai4Evg2szsub0fvegwD+KGmG\npPE51mP+D9bpzJNZ2yIiJPWKtuGSNgR+A5wUES8Xbwf39PchIlYBu0oaAPwWeG+Ni9SpJB0GLI6I\nGZL2r3V5auj9EbFQ0n8AUyU9UlzZ3f8PfOVSe8/nS1jyz8U5vhDYurDd4BxrKz64hXiXI6kvKbFM\njIibc7jXvQ8R8RIwDdibdJuj9GWvWOZ/v868/p3AC7T/felK9gU+Kmk+6ZbVAcBF9K73gIhYmH8u\nJn3J2JMe9H/g5FJ7k4FSC49xwC2F+NG5lcgoYFm+XJ4CjJa0SW5JMhqYkte9LGlUvh99dOFYXUYu\n25XAvIj438KqXvE+SBqYr1iQtD7wYVK90zTgyLxZ89dfel+OBO7K99AnA0flllTbAMNJFbgPAMNz\ny6t1SRXgk6v/ysoXEadFxOCIGEYq310RMZZe9B5I2kDSRqV50t/vHHrS/0Fnth7o7RPwK2AR8Bbp\nHuixpHvHdwKPA38CNs3bCriEdD9+NlBXOM4XSJWXDcAxhXgd6Q/0CeD/yE9g6EoT8H7SveZZwMw8\nHdJb3gdgF+DB/PrnAN/N8XeTPhgbgF8D/XJ8vbzckNe/u3Cs0/NrfJRCS6D8fj6W151e69e8hvdj\nfxpbi/Wa9yC/1ofyNLdUxp70f+DHv5iZWcX5tpiZmVWck4uZmVWck4uZmVWck4uZmVWck4uZmVWc\nk4v1SpI2y0+jnSnpOUkLC8vrdnJZFpT6vlTgWPdI2rUa20saKWlMx0tnvYkf/2K9UkS8AOwKIOks\nYHlEnF/TQnV9I4GdgDtqXRDr+nzlYtaMpN/nhwnOlfTFHFtH0kuS/jfHp0jaS9JfJD0p6ZC83baS\n7lYap2SGpL1y/CBJd0q6WWmckWubnfakvM8sSe/J+2wuaXKO/V3STi2Utb+kX0uaJ+k3pA6HpXWf\nVRrPY46kc9vx+teXdE3e9x+S9stPE/guMDZf3R0paUNJVyuNT/OgpP9s73ttPZeTi9nbjYuI3YE9\ngG/mx2pAeqbV7RGxI7ACOAs4EPgE8P28zSLgwxGxGzAWuLhw3JHACaRxSHbIj/EoeT7vcwVpnBOA\ns0njduySz3V1C2U9AVgaETsA55CeMl16pP05wIdybF+lB0aW4+vAmxGxM/A54DpgVX6NEyNi14i4\niZRs7oiIPUnPB/uJpPVaO6j1Lk4uZm/3DUkPAfeSHvi3bY6/HhFT8/xs4M+RHgE/GxiW4/2AK5VG\nG51ESiQl0yPin5GeijyzsA9A6QGeMwrx95M+2ImIPwJb5edQFe0HXJ+3eZD0KBGAvUjP4PpXRLxF\nGpRrvzJf//sLx5wL/BPYroXtRgOnKw0fMI101TSkzHNYD+c6F7MCSQeRPoRHRcTrku6h8VbTisKm\nq4E3C/Ol/6WTSeNrfJY0ENjywj5vFuZX0fT/781W4l2ZgCMi4olaF8S6Hl+5mDX1TuDFnFh2JN0a\na+/+iyI9tG8c6QO4o+4m3VorJb2FEfFqs23+Cnwmb/M+YMccvw/4UG4Vtw7pycB/6cB5dyANTd0A\nvEIamrpkCvC10oKk3cp+ZdbjObmYNfUHoL+kh0l1Fve1c///A76Yb6ttQ9Orlfb6LrC3pFmk+o5j\nWjnfZpLmAWeQnrhMRCzIy38m3YKbHhF/aOU8U3Jz6AWSfgX8FFhf0mxgInB0pOGC7wLelyvvjwS+\nB2yQK/7nkuqFzAD8VGQzM6s8X7mYmVnFObmYmVnFObmYmVnFObmYmVnFObmYmVnFObmYmVnFObmY\nmVnF/X8AVoupKS8+XwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "hDpjk2hPqBar"
-      },
-      "source": [
-        "###  Modelo - Regressão Polynomial"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "5ZQOFczoqBat"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "07250634-6377-453d-e7e3-ef60148b086e",
-        "id": "TARpu6xZqBat",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "source": [
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SAyttRhcqsz_",
-        "colab_type": "text"
-      },
-      "source": [
-        "Transformando as features na quarta ordem polinomial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "OZyCBmL2qs-X",
-        "colab_type": "code",
-        "outputId": "509a28e7-a7bb-4290-cd72-3851df815bdc",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "from sklearn.preprocessing import PolynomialFeatures\n",
-        "\n",
-        "poly_reg = PolynomialFeatures(degree = 4)\n",
-        "X_poly = poly_reg.fit_transform(X)\n",
-        "poly_reg.fit(X_poly, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "PolynomialFeatures(degree=4, include_bias=True, interaction_only=False,\n",
-              "                   order='C')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "92IzIbgrrDdG",
-        "colab_type": "text"
-      },
-      "source": [
-        "Treinando o modelo de regressão polynomial com o conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AiAG4XSPrDy9",
-        "colab_type": "code",
-        "outputId": "0c220401-e443-4669-e878-a9025f1455cb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "source": [
-        "lin_reg_poly = LinearRegression()\n",
-        "lin_reg_poly.fit(X_poly, y)\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 18
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "5Xy5SEAuqBaz"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "c16cdc19-73c9-4409-c400-ce89852480ca",
-        "id": "ySpZ9RyWqBa0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y_pred = lin_reg_poly.predict(X_poly)\n",
-        "\n",
-        "y_pred[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[210977.15912637],\n",
-              "       [101264.45016613],\n",
-              "       [231210.99529702],\n",
-              "       [242927.64582841],\n",
-              "       [195940.94028164]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 19
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wUrWgrvU0uTq",
-        "colab_type": "text"
-      },
-      "source": [
-        "Avaliando o modelo de regressão linear com a métrica r2:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lETAHBqw0uC5",
-        "colab_type": "code",
-        "outputId": "f7368a63-cfcc-49ae-bdde-f1664f40d0f6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.14645648066355865"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "f8hyc24cqBa7"
-      },
-      "source": [
-        "Avaliando o modelo de regressão polynomial com a métrica r2:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "b77eb92e-2960-4140-de80-b473142322e3",
-        "id": "HDXwQ0ddqBa9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "lin_reg_poly.score(X_poly, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.2824360477057858"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 24
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "gaRZCCjKqBbC"
-      },
-      "source": [
-        "Visualizando os resultados da regressão linear:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "cd930c49-89d7-4214-ef23-73663647d20e",
-        "id": "GdApU1gYqBbD",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_linear(X, y, regressor, 'Regressão Linear')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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NgQFV1bnpCmu//WDBgqaxhobmQ7i4zuO/eldYVT4EhiusSy8NtZVkYlm9OtRWPLEUl9dc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\n62UAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "LlX6EwYwqBbG"
-      },
-      "source": [
-        "Visualizando os resultados da regressão linear polinomial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "6e05d3f9-7557-4788-f409-316133d351ae",
-        "id": "5GKwud8fqBbG",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_poly(X, y, lin_reg_poly, poly_reg, 'Regressão Polynomial')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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ptp13hv/9zxJIXnCBKZZNm2w7nT32gKOPhrlzLZXLnDnmHe6Kxam1JForK1uw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\nTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "fB8cnF96qcQM"
-      },
-      "source": [
-        "###  Modelo - Árvore de Decisão "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "sgo7wyZUqcQQ"
-      },
-      "source": [
-        "Treinando o modelo de Arvore de Decisão  com o Conjunto de Treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "2f83d111-579f-4617-bbc7-4d1441914581",
-        "id": "-T3_UnXvqcQR",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "\n",
-        "regressor = DecisionTreeRegressor(random_state = 0)\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
-              "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
-              "                      min_impurity_split=None, min_samples_leaf=1,\n",
-              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
-              "                      presort=False, random_state=0, splitter='best')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 27
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "isKict8VqcQW"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "dc307c8a-b1b9-4c8e-cb8e-f49e749e050d",
-        "id": "myt4FBHpqcQX",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([195000., 115000.,  94000., 216500., 139400., 113000., 214900.,\n",
-              "       402000., 195000., 150750., 148000., 215200.])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 28
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "y0HxOT1nqcQa"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "b802e761-2c5c-465b-ca55-f42f5e2fd0a5",
-        "id": "EXTaMCEuqcQb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "-1.1563461633885121"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 29
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "-8m22r5SqcQd"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "41bc6a12-484d-483c-8221-ed9b43f54116",
-        "id": "HTJnv_R9qcQe",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_reg(X_train, y_train, regressor, 'Arvore de Decisão (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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2Ss0qlAMPhMcfB4Z/LQyFpVNTA48vaAy++GJIU3bEbY4zytNo8x98wABY16+Z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\n4zgFx5WL4ziOU3BcuTiO4zgF5/8DyR4QJtjHHvsAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "XviIofWNqcQj"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "7c7c2439-349e-401f-d0bc-e9b58d2f1461",
-        "id": "57h8qL4EqcQk",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_reg(X_test, y_test, regressor, 'Arvore de Decisão (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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iZjZD0nOEqi0B55nZe8VmIGlr4B7gu2a2RtL1wGUET+gy4Grg680pfEuRNAGY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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "MyrmVTcFtw9L"
-      },
-      "source": [
-        "###  Modelo - Random Forest"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "-qBwfwdatw9N"
-      },
-      "source": [
-        "Treinando o modelo de Random Forest com o Conjunto de Treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "d1891c7a-7e6c-4100-9d7d-0e3710b5f410",
-        "id": "2G9vzxcatw9N",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "from sklearn.ensemble import RandomForestRegressor\n",
-        "\n",
-        "regressor = DecisionTreeRegressor(random_state = 0)\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
-              "                      max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
-              "                      min_impurity_split=None, min_samples_leaf=1,\n",
-              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
-              "                      presort=False, random_state=0, splitter='best')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 32
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "fl4bh74Ttw9T"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "ce1fa1f1-cae6-4b89-d8c3-acbac1577ecc",
-        "id": "v4a4FEGktw9T",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([195000., 115000.,  94000., 216500., 139400., 113000., 214900.,\n",
-              "       402000., 195000., 150750., 148000., 215200.])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 33
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "avqChi46tw9W"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "8b77d5e2-6ccd-4026-e7af-f022d699efd3",
-        "id": "XCRy0Ekktw9X",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "-1.1563461633885121"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 34
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "iykUlZD0tw9d"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "e69ea18d-464a-4bae-85e6-01217c536f86",
-        "id": "QPK8C0Fgtw9e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "plot_results_reg(X_train, y_train, regressor, 'Random Forest Regression (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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odDH8kXYc4K674PK4XZWK2UfQvpRbvo7Gjt6WooidBsceC4cfXlwYx+ksuG0xxwG2xx3S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\nXLk4juM4qePKxXEcx0md/w+cUvYyu3QqiwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "frp-AsLstw9l"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "fd1587db-da4b-40d1-c3ea-3f1faad27bee",
-        "id": "hgES4om6tw9m",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "plot_results_reg(X_test, y_test, regressor, 'Random Forest Regression (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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M7FmgkbDszEig0cwWmtmbwFRgrCQBo4FbY/zJwMmJtCbH81uBo2P4uqASlkux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\ncnEcx3Eyx5WL4ziOkzmuXBzHcZzM+f8HxrgJdLSR2QAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "Ood5PUZCtxsa"
-      },
-      "source": [
-        "###  Modelo - SVR"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Nd_nx5-ju7Wh",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalização das features:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "isa4YR-Mu7gd",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X_train = feature_scaling(X_train)\n",
-        "X_test = feature_scaling(X_test)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "TYZy4UgYtxsc"
-      },
-      "source": [
-        "Treinando o modelo SVR com o Conjunto de Treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "435c97fb-d4c5-446e-8738-6e10dd173e4f",
-        "id": "OSS7HBwstxsd",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "from sklearn.svm import SVR\n",
-        "\n",
-        "regressor = SVR(kernel = 'rbf')\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-            "  y = column_or_1d(y, warn=True)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
-              "    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
-              "    tol=0.001, verbose=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 38
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pjZ3LL7jvCv2",
-        "colab_type": "text"
-      },
-      "source": [
-        "Prevendo os resultados com o conjunto de testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "1e4971fd-6234-47a4-f9df-50b66f96884b",
-        "id": "HvFjHJ76txsl",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([194251.44370433, 194247.70187487, 194245.70018159, 194247.1367784 ,\n",
-              "       194248.45357491, 194249.01439172, 194250.67281124, 194249.47673342,\n",
-              "       194251.37775764, 194246.60246747, 194249.61450541, 194248.02383322])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 39
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "UnULaDnVtxsr"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "881ac17f-0128-4014-a6a9-4b04e0ce0c63",
-        "id": "ULsy2S4Htxsx",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "-0.041556703288624375"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 40
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "T4L09wqRtxs2"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "2ee9f048-a7a4-43e0-a204-46b9f25aeb40",
-        "id": "4dRGKOeXtxs4",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "plot_results_reg(X_train, y_train, regressor, 'SVR (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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A2Bc4HliWN1sGnJA/Hw9cFclKYA9J+wDvBFZExMaIeBpYASzM66ZHxMrcwfOq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\nM7OG+/8rBrr9cW3qtAAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "d91HUwEXtxs-"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "62ad4c35-8e5a-4f4b-ea1c-5fa2d896115e",
-        "id": "OoqXeIfBtxtA",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "plot_results_reg(X_test, y_test, regressor, 'Arvore de Decisão (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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1wAjggoiYl3fxReASSWcCdwHn5/LzgZ9I6iZdUHBMo15jQ1V+nX/qqakpbOzY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\nk4uZmZXu/wP4AUFGM0WJIwAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "VUZAwJjMvh0r"
-      },
-      "source": [
-        "###  Modelo - Regressão Linear Múltipla"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZOIXAi6Wvpce",
-        "colab_type": "text"
-      },
-      "source": [
-        "Selecionando as principais features do dataset:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "et8CNkI1vpk2",
-        "colab_type": "code",
-        "outputId": "ce479ea9-3f72-4269-d0f0-ac7139dbe9ce",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>661</th>\n",
-              "      <td>46589</td>\n",
-              "      <td>0</td>\n",
-              "      <td>711</td>\n",
-              "      <td>7</td>\n",
-              "      <td>1994</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>402000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>964</th>\n",
-              "      <td>11316</td>\n",
-              "      <td>0</td>\n",
-              "      <td>510</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2002</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>214900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>891</th>\n",
-              "      <td>11184</td>\n",
-              "      <td>0</td>\n",
-              "      <td>440</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>172500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>926</th>\n",
-              "      <td>11999</td>\n",
-              "      <td>0</td>\n",
-              "      <td>656</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>285000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1374</th>\n",
-              "      <td>10625</td>\n",
-              "      <td>0</td>\n",
-              "      <td>936</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2005</td>\n",
-              "      <td>FV</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "661     46589         0         711            7       1994       RL     402000\n",
-              "964     11316         0         510            5       2002       RL     214900\n",
-              "891     11184         0         440            5       1978       RL     172500\n",
-              "926     11999         0         656            5       2003       RL     285000\n",
-              "1374    10625         0         936            5       2005       FV     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZjC2LJYh1SQ6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
-        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SeWh_rpnYkLd",
-        "colab_type": "code",
-        "outputId": "3ab29a73-ecb7-4b37-efc1-b1f41db60644",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "df['MSZoning'].unique()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['RL', 'FV', 'RM', 'RH'], dtype=object)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eumBvAB8wbq-",
-        "colab_type": "text"
-      },
-      "source": [
-        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rjT1ONG7wb9x",
-        "colab_type": "code",
-        "outputId": "26d1bcf1-6143-4e6c-a7b2-e7820292f635",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 224
-        }
-      },
-      "source": [
-        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "      <th>MSZoning_RH</th>\n",
-              "      <th>MSZoning_RL</th>\n",
-              "      <th>MSZoning_RM</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>661</th>\n",
-              "      <td>46589</td>\n",
-              "      <td>0</td>\n",
-              "      <td>711</td>\n",
-              "      <td>7</td>\n",
-              "      <td>1994</td>\n",
-              "      <td>402000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>964</th>\n",
-              "      <td>11316</td>\n",
-              "      <td>0</td>\n",
-              "      <td>510</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2002</td>\n",
-              "      <td>214900</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>891</th>\n",
-              "      <td>11184</td>\n",
-              "      <td>0</td>\n",
-              "      <td>440</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>172500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>926</th>\n",
-              "      <td>11999</td>\n",
-              "      <td>0</td>\n",
-              "      <td>656</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>285000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1374</th>\n",
-              "      <td>10625</td>\n",
-              "      <td>0</td>\n",
-              "      <td>936</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2005</td>\n",
-              "      <td>250000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
-              "661     46589         0         711  ...            0            1            0\n",
-              "964     11316         0         510  ...            0            1            0\n",
-              "891     11184         0         440  ...            0            1            0\n",
-              "926     11999         0         656  ...            0            1            0\n",
-              "1374    10625         0         936  ...            0            0            0\n",
-              "\n",
-              "[5 rows x 9 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 44
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i9fs5x8MvpxV",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "b9uRFW2vvp6b",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
-        "y = df['SalePrice'].values.reshape(-1,1)\n",
-        "\n",
-        "# Dividindo os dados\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "SMaN0iHNvh0t"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "6cbc7612-fa1d-4078-a14f-c2e943dcf113",
-        "id": "VuzaRxyuvh0u",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 54
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "\n",
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 46
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "zT6PUXkWvh0y"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "86f38ad3-ebc8-4b0a-9f8a-fccd2ce0fbd3",
-        "id": "JR-E180Fvh0y",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 221
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[376052.98497919],\n",
-              "       [185871.16123409],\n",
-              "       [230508.60986049],\n",
-              "       [269278.25223188],\n",
-              "       [153039.2404638 ],\n",
-              "       [ 88676.03962536],\n",
-              "       [210563.82989375],\n",
-              "       [131945.37958412],\n",
-              "       [204656.51558602],\n",
-              "       [226768.14382706],\n",
-              "       [229229.28285228],\n",
-              "       [216857.75545174]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 47
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "PO6sGI3Gvh01"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "a5233703-1980-459b-f44f-526e66329bd4",
-        "id": "fQPgnd8dvh02",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.7460190735464488"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 48
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KDcimzDmxxIo",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Estudo de Caso: Pipeline de Regressão"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g_qAJYI2x4DL",
-        "colab_type": "text"
-      },
-      "source": [
-        "**Descrição do Problema**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JoB6nJpox8AP",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando as packages"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "C9rs16Mzx8ec",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.linear_model import LinearRegression\n",
-        "from sklearn.tree import DecisionTreeRegressor\n",
-        "from sklearn.ensemble import RandomForestRegressor\n",
-        "from sklearn.svm import SVR\n",
-        "from sklearn.metrics import mean_squared_error, mean_squared_log_error"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VNaNeCy_yEet",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando os dados"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "OCPXPwhNyEp2",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/r4phael/ml-course/master/data/pricing_houses.csv')\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "COuAxCI4yFCb",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando e descrevendo  o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Y-yKSlG_yFRs",
-        "colab_type": "code",
-        "outputId": "f6bb9000-88f4-45b1-9386-2ffeb4f5f31d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        }
-      },
-      "source": [
-        "df.info()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 1460 entries, 0 to 1459\n",
-            "Data columns (total 81 columns):\n",
-            "Id               1460 non-null int64\n",
-            "MSSubClass       1460 non-null int64\n",
-            "MSZoning         1460 non-null object\n",
-            "LotFrontage      1201 non-null float64\n",
-            "LotArea          1460 non-null int64\n",
-            "Street           1460 non-null object\n",
-            "Alley            91 non-null object\n",
-            "LotShape         1460 non-null object\n",
-            "LandContour      1460 non-null object\n",
-            "Utilities        1460 non-null object\n",
-            "LotConfig        1460 non-null object\n",
-            "LandSlope        1460 non-null object\n",
-            "Neighborhood     1460 non-null object\n",
-            "Condition1       1460 non-null object\n",
-            "Condition2       1460 non-null object\n",
-            "BldgType         1460 non-null object\n",
-            "HouseStyle       1460 non-null object\n",
-            "OverallQual      1460 non-null int64\n",
-            "OverallCond      1460 non-null int64\n",
-            "YearBuilt        1460 non-null int64\n",
-            "YearRemodAdd     1460 non-null int64\n",
-            "RoofStyle        1460 non-null object\n",
-            "RoofMatl         1460 non-null object\n",
-            "Exterior1st      1460 non-null object\n",
-            "Exterior2nd      1460 non-null object\n",
-            "MasVnrType       1452 non-null object\n",
-            "MasVnrArea       1452 non-null float64\n",
-            "ExterQual        1460 non-null object\n",
-            "ExterCond        1460 non-null object\n",
-            "Foundation       1460 non-null object\n",
-            "BsmtQual         1423 non-null object\n",
-            "BsmtCond         1423 non-null object\n",
-            "BsmtExposure     1422 non-null object\n",
-            "BsmtFinType1     1423 non-null object\n",
-            "BsmtFinSF1       1460 non-null int64\n",
-            "BsmtFinType2     1422 non-null object\n",
-            "BsmtFinSF2       1460 non-null int64\n",
-            "BsmtUnfSF        1460 non-null int64\n",
-            "TotalBsmtSF      1460 non-null int64\n",
-            "Heating          1460 non-null object\n",
-            "HeatingQC        1460 non-null object\n",
-            "CentralAir       1460 non-null object\n",
-            "Electrical       1459 non-null object\n",
-            "1stFlrSF         1460 non-null int64\n",
-            "2ndFlrSF         1460 non-null int64\n",
-            "LowQualFinSF     1460 non-null int64\n",
-            "GrLivArea        1460 non-null int64\n",
-            "BsmtFullBath     1460 non-null int64\n",
-            "BsmtHalfBath     1460 non-null int64\n",
-            "FullBath         1460 non-null int64\n",
-            "HalfBath         1460 non-null int64\n",
-            "BedroomAbvGr     1460 non-null int64\n",
-            "KitchenAbvGr     1460 non-null int64\n",
-            "KitchenQual      1460 non-null object\n",
-            "TotRmsAbvGrd     1460 non-null int64\n",
-            "Functional       1460 non-null object\n",
-            "Fireplaces       1460 non-null int64\n",
-            "FireplaceQu      770 non-null object\n",
-            "GarageType       1379 non-null object\n",
-            "GarageYrBlt      1379 non-null float64\n",
-            "GarageFinish     1379 non-null object\n",
-            "GarageCars       1460 non-null int64\n",
-            "GarageArea       1460 non-null int64\n",
-            "GarageQual       1379 non-null object\n",
-            "GarageCond       1379 non-null object\n",
-            "PavedDrive       1460 non-null object\n",
-            "WoodDeckSF       1460 non-null int64\n",
-            "OpenPorchSF      1460 non-null int64\n",
-            "EnclosedPorch    1460 non-null int64\n",
-            "3SsnPorch        1460 non-null int64\n",
-            "ScreenPorch      1460 non-null int64\n",
-            "PoolArea         1460 non-null int64\n",
-            "PoolQC           7 non-null object\n",
-            "Fence            281 non-null object\n",
-            "MiscFeature      54 non-null object\n",
-            "MiscVal          1460 non-null int64\n",
-            "MoSold           1460 non-null int64\n",
-            "YrSold           1460 non-null int64\n",
-            "SaleType         1460 non-null object\n",
-            "SaleCondition    1460 non-null object\n",
-            "SalePrice        1460 non-null int64\n",
-            "dtypes: float64(3), int64(35), object(43)\n",
-            "memory usage: 924.0+ KB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "HxPPf4CPySYK",
-        "colab_type": "code",
-        "outputId": "63daec2c-cc09-44dc-bd13-6eff7d7727ce",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 253
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Id</th>\n",
-              "      <th>MSSubClass</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>LotFrontage</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>Street</th>\n",
-              "      <th>Alley</th>\n",
-              "      <th>LotShape</th>\n",
-              "      <th>LandContour</th>\n",
-              "      <th>Utilities</th>\n",
-              "      <th>LotConfig</th>\n",
-              "      <th>LandSlope</th>\n",
-              "      <th>Neighborhood</th>\n",
-              "      <th>Condition1</th>\n",
-              "      <th>Condition2</th>\n",
-              "      <th>BldgType</th>\n",
-              "      <th>HouseStyle</th>\n",
-              "      <th>OverallQual</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>YearRemodAdd</th>\n",
-              "      <th>RoofStyle</th>\n",
-              "      <th>RoofMatl</th>\n",
-              "      <th>Exterior1st</th>\n",
-              "      <th>Exterior2nd</th>\n",
-              "      <th>MasVnrType</th>\n",
-              "      <th>MasVnrArea</th>\n",
-              "      <th>ExterQual</th>\n",
-              "      <th>ExterCond</th>\n",
-              "      <th>Foundation</th>\n",
-              "      <th>BsmtQual</th>\n",
-              "      <th>BsmtCond</th>\n",
-              "      <th>BsmtExposure</th>\n",
-              "      <th>BsmtFinType1</th>\n",
-              "      <th>BsmtFinSF1</th>\n",
-              "      <th>BsmtFinType2</th>\n",
-              "      <th>BsmtFinSF2</th>\n",
-              "      <th>BsmtUnfSF</th>\n",
-              "      <th>TotalBsmtSF</th>\n",
-              "      <th>Heating</th>\n",
-              "      <th>...</th>\n",
-              "      <th>CentralAir</th>\n",
-              "      <th>Electrical</th>\n",
-              "      <th>1stFlrSF</th>\n",
-              "      <th>2ndFlrSF</th>\n",
-              "      <th>LowQualFinSF</th>\n",
-              "      <th>GrLivArea</th>\n",
-              "      <th>BsmtFullBath</th>\n",
-              "      <th>BsmtHalfBath</th>\n",
-              "      <th>FullBath</th>\n",
-              "      <th>HalfBath</th>\n",
-              "      <th>BedroomAbvGr</th>\n",
-              "      <th>KitchenAbvGr</th>\n",
-              "      <th>KitchenQual</th>\n",
-              "      <th>TotRmsAbvGrd</th>\n",
-              "      <th>Functional</th>\n",
-              "      <th>Fireplaces</th>\n",
-              "      <th>FireplaceQu</th>\n",
-              "      <th>GarageType</th>\n",
-              "      <th>GarageYrBlt</th>\n",
-              "      <th>GarageFinish</th>\n",
-              "      <th>GarageCars</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>GarageQual</th>\n",
-              "      <th>GarageCond</th>\n",
-              "      <th>PavedDrive</th>\n",
-              "      <th>WoodDeckSF</th>\n",
-              "      <th>OpenPorchSF</th>\n",
-              "      <th>EnclosedPorch</th>\n",
-              "      <th>3SsnPorch</th>\n",
-              "      <th>ScreenPorch</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>PoolQC</th>\n",
-              "      <th>Fence</th>\n",
-              "      <th>MiscFeature</th>\n",
-              "      <th>MiscVal</th>\n",
-              "      <th>MoSold</th>\n",
-              "      <th>YrSold</th>\n",
-              "      <th>SaleType</th>\n",
-              "      <th>SaleCondition</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>1</td>\n",
-              "      <td>60</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>65.0</td>\n",
-              "      <td>8450</td>\n",
-              "      <td>Pave</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>Reg</td>\n",
-              "      <td>Lvl</td>\n",
-              "      <td>AllPub</td>\n",
-              "      <td>Inside</td>\n",
-              "      <td>Gtl</td>\n",
-              "      <td>CollgCr</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>1Fam</td>\n",
-              "      <td>2Story</td>\n",
-              "      <td>7</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>Gable</td>\n",
-              "      <td>CompShg</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>BrkFace</td>\n",
-              "      <td>196.0</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>PConc</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>No</td>\n",
-              "      <td>GLQ</td>\n",
-              "      <td>706</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>0</td>\n",
-              "      <td>150</td>\n",
-              "      <td>856</td>\n",
-              "      <td>GasA</td>\n",
-              "      <td>...</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>SBrkr</td>\n",
-              "      <td>856</td>\n",
-              "      <td>854</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1710</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>8</td>\n",
-              "      <td>Typ</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>Attchd</td>\n",
-              "      <td>2003.0</td>\n",
-              "      <td>RFn</td>\n",
-              "      <td>2</td>\n",
-              "      <td>548</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>0</td>\n",
-              "      <td>61</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2008</td>\n",
-              "      <td>WD</td>\n",
-              "      <td>Normal</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>2</td>\n",
-              "      <td>20</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>80.0</td>\n",
-              "      <td>9600</td>\n",
-              "      <td>Pave</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>Reg</td>\n",
-              "      <td>Lvl</td>\n",
-              "      <td>AllPub</td>\n",
-              "      <td>FR2</td>\n",
-              "      <td>Gtl</td>\n",
-              "      <td>Veenker</td>\n",
-              "      <td>Feedr</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>1Fam</td>\n",
-              "      <td>1Story</td>\n",
-              "      <td>6</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>Gable</td>\n",
-              "      <td>CompShg</td>\n",
-              "      <td>MetalSd</td>\n",
-              "      <td>MetalSd</td>\n",
-              "      <td>None</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>CBlock</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>ALQ</td>\n",
-              "      <td>978</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>0</td>\n",
-              "      <td>284</td>\n",
-              "      <td>1262</td>\n",
-              "      <td>GasA</td>\n",
-              "      <td>...</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>SBrkr</td>\n",
-              "      <td>1262</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1262</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Typ</td>\n",
-              "      <td>1</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Attchd</td>\n",
-              "      <td>1976.0</td>\n",
-              "      <td>RFn</td>\n",
-              "      <td>2</td>\n",
-              "      <td>460</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>298</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2007</td>\n",
-              "      <td>WD</td>\n",
-              "      <td>Normal</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>3</td>\n",
-              "      <td>60</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>68.0</td>\n",
-              "      <td>11250</td>\n",
-              "      <td>Pave</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>IR1</td>\n",
-              "      <td>Lvl</td>\n",
-              "      <td>AllPub</td>\n",
-              "      <td>Inside</td>\n",
-              "      <td>Gtl</td>\n",
-              "      <td>CollgCr</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>1Fam</td>\n",
-              "      <td>2Story</td>\n",
-              "      <td>7</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>2002</td>\n",
-              "      <td>Gable</td>\n",
-              "      <td>CompShg</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>BrkFace</td>\n",
-              "      <td>162.0</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>PConc</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Mn</td>\n",
-              "      <td>GLQ</td>\n",
-              "      <td>486</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>0</td>\n",
-              "      <td>434</td>\n",
-              "      <td>920</td>\n",
-              "      <td>GasA</td>\n",
-              "      <td>...</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>SBrkr</td>\n",
-              "      <td>920</td>\n",
-              "      <td>866</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1786</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>6</td>\n",
-              "      <td>Typ</td>\n",
-              "      <td>1</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Attchd</td>\n",
-              "      <td>2001.0</td>\n",
-              "      <td>RFn</td>\n",
-              "      <td>2</td>\n",
-              "      <td>608</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>0</td>\n",
-              "      <td>42</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>9</td>\n",
-              "      <td>2008</td>\n",
-              "      <td>WD</td>\n",
-              "      <td>Normal</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>4</td>\n",
-              "      <td>70</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>60.0</td>\n",
-              "      <td>9550</td>\n",
-              "      <td>Pave</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>IR1</td>\n",
-              "      <td>Lvl</td>\n",
-              "      <td>AllPub</td>\n",
-              "      <td>Corner</td>\n",
-              "      <td>Gtl</td>\n",
-              "      <td>Crawfor</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>1Fam</td>\n",
-              "      <td>2Story</td>\n",
-              "      <td>7</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>1970</td>\n",
-              "      <td>Gable</td>\n",
-              "      <td>CompShg</td>\n",
-              "      <td>Wd Sdng</td>\n",
-              "      <td>Wd Shng</td>\n",
-              "      <td>None</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>BrkTil</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>No</td>\n",
-              "      <td>ALQ</td>\n",
-              "      <td>216</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>0</td>\n",
-              "      <td>540</td>\n",
-              "      <td>756</td>\n",
-              "      <td>GasA</td>\n",
-              "      <td>...</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>SBrkr</td>\n",
-              "      <td>961</td>\n",
-              "      <td>756</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1717</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>7</td>\n",
-              "      <td>Typ</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>Detchd</td>\n",
-              "      <td>1998.0</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>3</td>\n",
-              "      <td>642</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>0</td>\n",
-              "      <td>35</td>\n",
-              "      <td>272</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2006</td>\n",
-              "      <td>WD</td>\n",
-              "      <td>Abnorml</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>5</td>\n",
-              "      <td>60</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>84.0</td>\n",
-              "      <td>14260</td>\n",
-              "      <td>Pave</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>IR1</td>\n",
-              "      <td>Lvl</td>\n",
-              "      <td>AllPub</td>\n",
-              "      <td>FR2</td>\n",
-              "      <td>Gtl</td>\n",
-              "      <td>NoRidge</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>Norm</td>\n",
-              "      <td>1Fam</td>\n",
-              "      <td>2Story</td>\n",
-              "      <td>8</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>Gable</td>\n",
-              "      <td>CompShg</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>VinylSd</td>\n",
-              "      <td>BrkFace</td>\n",
-              "      <td>350.0</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>PConc</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Av</td>\n",
-              "      <td>GLQ</td>\n",
-              "      <td>655</td>\n",
-              "      <td>Unf</td>\n",
-              "      <td>0</td>\n",
-              "      <td>490</td>\n",
-              "      <td>1145</td>\n",
-              "      <td>GasA</td>\n",
-              "      <td>...</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>SBrkr</td>\n",
-              "      <td>1145</td>\n",
-              "      <td>1053</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2198</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Gd</td>\n",
-              "      <td>9</td>\n",
-              "      <td>Typ</td>\n",
-              "      <td>1</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Attchd</td>\n",
-              "      <td>2000.0</td>\n",
-              "      <td>RFn</td>\n",
-              "      <td>3</td>\n",
-              "      <td>836</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>TA</td>\n",
-              "      <td>Y</td>\n",
-              "      <td>192</td>\n",
-              "      <td>84</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>12</td>\n",
-              "      <td>2008</td>\n",
-              "      <td>WD</td>\n",
-              "      <td>Normal</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>5 rows × 81 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice\n",
-              "0   1          60       RL  ...        WD         Normal    208500\n",
-              "1   2          20       RL  ...        WD         Normal    181500\n",
-              "2   3          60       RL  ...        WD         Normal    223500\n",
-              "3   4          70       RL  ...        WD        Abnorml    140000\n",
-              "4   5          60       RL  ...        WD         Normal    250000\n",
-              "\n",
-              "[5 rows x 81 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 56
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TbazA1fRyFcB",
-        "colab_type": "text"
-      },
-      "source": [
-        "Selecionando apenas as features numericas"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "BZt4EL9_yFmM",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = df.select_dtypes(include=['int64', 'float64'])"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "-tG-F_aQyFtX",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "0vDXGj1IyF3Z",
-        "colab_type": "code",
-        "outputId": "54e8cf94-1629-434b-b240-445a0a8cccb8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 317
-        }
-      },
-      "source": [
-        "df.describe()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Id</th>\n",
-              "      <th>MSSubClass</th>\n",
-              "      <th>LotFrontage</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>OverallQual</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>YearRemodAdd</th>\n",
-              "      <th>MasVnrArea</th>\n",
-              "      <th>BsmtFinSF1</th>\n",
-              "      <th>BsmtFinSF2</th>\n",
-              "      <th>BsmtUnfSF</th>\n",
-              "      <th>TotalBsmtSF</th>\n",
-              "      <th>1stFlrSF</th>\n",
-              "      <th>2ndFlrSF</th>\n",
-              "      <th>LowQualFinSF</th>\n",
-              "      <th>GrLivArea</th>\n",
-              "      <th>BsmtFullBath</th>\n",
-              "      <th>BsmtHalfBath</th>\n",
-              "      <th>FullBath</th>\n",
-              "      <th>HalfBath</th>\n",
-              "      <th>BedroomAbvGr</th>\n",
-              "      <th>KitchenAbvGr</th>\n",
-              "      <th>TotRmsAbvGrd</th>\n",
-              "      <th>Fireplaces</th>\n",
-              "      <th>GarageYrBlt</th>\n",
-              "      <th>GarageCars</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>WoodDeckSF</th>\n",
-              "      <th>OpenPorchSF</th>\n",
-              "      <th>EnclosedPorch</th>\n",
-              "      <th>3SsnPorch</th>\n",
-              "      <th>ScreenPorch</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>MiscVal</th>\n",
-              "      <th>MoSold</th>\n",
-              "      <th>YrSold</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1201.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1452.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1379.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>1460.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>730.500000</td>\n",
-              "      <td>56.897260</td>\n",
-              "      <td>70.049958</td>\n",
-              "      <td>10516.828082</td>\n",
-              "      <td>6.099315</td>\n",
-              "      <td>5.575342</td>\n",
-              "      <td>1971.267808</td>\n",
-              "      <td>1984.865753</td>\n",
-              "      <td>103.685262</td>\n",
-              "      <td>443.639726</td>\n",
-              "      <td>46.549315</td>\n",
-              "      <td>567.240411</td>\n",
-              "      <td>1057.429452</td>\n",
-              "      <td>1162.626712</td>\n",
-              "      <td>346.992466</td>\n",
-              "      <td>5.844521</td>\n",
-              "      <td>1515.463699</td>\n",
-              "      <td>0.425342</td>\n",
-              "      <td>0.057534</td>\n",
-              "      <td>1.565068</td>\n",
-              "      <td>0.382877</td>\n",
-              "      <td>2.866438</td>\n",
-              "      <td>1.046575</td>\n",
-              "      <td>6.517808</td>\n",
-              "      <td>0.613014</td>\n",
-              "      <td>1978.506164</td>\n",
-              "      <td>1.767123</td>\n",
-              "      <td>472.980137</td>\n",
-              "      <td>94.244521</td>\n",
-              "      <td>46.660274</td>\n",
-              "      <td>21.954110</td>\n",
-              "      <td>3.409589</td>\n",
-              "      <td>15.060959</td>\n",
-              "      <td>2.758904</td>\n",
-              "      <td>43.489041</td>\n",
-              "      <td>6.321918</td>\n",
-              "      <td>2007.815753</td>\n",
-              "      <td>180921.195890</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>421.610009</td>\n",
-              "      <td>42.300571</td>\n",
-              "      <td>24.284752</td>\n",
-              "      <td>9981.264932</td>\n",
-              "      <td>1.382997</td>\n",
-              "      <td>1.112799</td>\n",
-              "      <td>30.202904</td>\n",
-              "      <td>20.645407</td>\n",
-              "      <td>181.066207</td>\n",
-              "      <td>456.098091</td>\n",
-              "      <td>161.319273</td>\n",
-              "      <td>441.866955</td>\n",
-              "      <td>438.705324</td>\n",
-              "      <td>386.587738</td>\n",
-              "      <td>436.528436</td>\n",
-              "      <td>48.623081</td>\n",
-              "      <td>525.480383</td>\n",
-              "      <td>0.518911</td>\n",
-              "      <td>0.238753</td>\n",
-              "      <td>0.550916</td>\n",
-              "      <td>0.502885</td>\n",
-              "      <td>0.815778</td>\n",
-              "      <td>0.220338</td>\n",
-              "      <td>1.625393</td>\n",
-              "      <td>0.644666</td>\n",
-              "      <td>24.689725</td>\n",
-              "      <td>0.747315</td>\n",
-              "      <td>213.804841</td>\n",
-              "      <td>125.338794</td>\n",
-              "      <td>66.256028</td>\n",
-              "      <td>61.119149</td>\n",
-              "      <td>29.317331</td>\n",
-              "      <td>55.757415</td>\n",
-              "      <td>40.177307</td>\n",
-              "      <td>496.123024</td>\n",
-              "      <td>2.703626</td>\n",
-              "      <td>1.328095</td>\n",
-              "      <td>79442.502883</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>20.000000</td>\n",
-              "      <td>21.000000</td>\n",
-              "      <td>1300.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1872.000000</td>\n",
-              "      <td>1950.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>334.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>334.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1900.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2006.000000</td>\n",
-              "      <td>34900.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>365.750000</td>\n",
-              "      <td>20.000000</td>\n",
-              "      <td>59.000000</td>\n",
-              "      <td>7553.500000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1954.000000</td>\n",
-              "      <td>1967.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>223.000000</td>\n",
-              "      <td>795.750000</td>\n",
-              "      <td>882.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1129.500000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1961.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>334.500000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>2007.000000</td>\n",
-              "      <td>129975.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>730.500000</td>\n",
-              "      <td>50.000000</td>\n",
-              "      <td>69.000000</td>\n",
-              "      <td>9478.500000</td>\n",
-              "      <td>6.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1973.000000</td>\n",
-              "      <td>1994.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>383.500000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>477.500000</td>\n",
-              "      <td>991.500000</td>\n",
-              "      <td>1087.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1464.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>6.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1980.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>480.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>25.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>6.000000</td>\n",
-              "      <td>2008.000000</td>\n",
-              "      <td>163000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>1095.250000</td>\n",
-              "      <td>70.000000</td>\n",
-              "      <td>80.000000</td>\n",
-              "      <td>11601.500000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>6.000000</td>\n",
-              "      <td>2000.000000</td>\n",
-              "      <td>2004.000000</td>\n",
-              "      <td>166.000000</td>\n",
-              "      <td>712.250000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>808.000000</td>\n",
-              "      <td>1298.250000</td>\n",
-              "      <td>1391.250000</td>\n",
-              "      <td>728.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1776.750000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>576.000000</td>\n",
-              "      <td>168.000000</td>\n",
-              "      <td>68.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>8.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>214000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>1460.000000</td>\n",
-              "      <td>190.000000</td>\n",
-              "      <td>313.000000</td>\n",
-              "      <td>215245.000000</td>\n",
-              "      <td>10.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2010.000000</td>\n",
-              "      <td>2010.000000</td>\n",
-              "      <td>1600.000000</td>\n",
-              "      <td>5644.000000</td>\n",
-              "      <td>1474.000000</td>\n",
-              "      <td>2336.000000</td>\n",
-              "      <td>6110.000000</td>\n",
-              "      <td>4692.000000</td>\n",
-              "      <td>2065.000000</td>\n",
-              "      <td>572.000000</td>\n",
-              "      <td>5642.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>8.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>14.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>2010.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>1418.000000</td>\n",
-              "      <td>857.000000</td>\n",
-              "      <td>547.000000</td>\n",
-              "      <td>552.000000</td>\n",
-              "      <td>508.000000</td>\n",
-              "      <td>480.000000</td>\n",
-              "      <td>738.000000</td>\n",
-              "      <td>15500.000000</td>\n",
-              "      <td>12.000000</td>\n",
-              "      <td>2010.000000</td>\n",
-              "      <td>755000.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                Id   MSSubClass  ...       YrSold      SalePrice\n",
-              "count  1460.000000  1460.000000  ...  1460.000000    1460.000000\n",
-              "mean    730.500000    56.897260  ...  2007.815753  180921.195890\n",
-              "std     421.610009    42.300571  ...     1.328095   79442.502883\n",
-              "min       1.000000    20.000000  ...  2006.000000   34900.000000\n",
-              "25%     365.750000    20.000000  ...  2007.000000  129975.000000\n",
-              "50%     730.500000    50.000000  ...  2008.000000  163000.000000\n",
-              "75%    1095.250000    70.000000  ...  2009.000000  214000.000000\n",
-              "max    1460.000000   190.000000  ...  2010.000000  755000.000000\n",
-              "\n",
-              "[8 rows x 38 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 58
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "mEfXmKH1yGCr",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "U0KsZkMZyGnW",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = df.fillna(df.median())\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TNhEpIuJyG0C",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis indepedentes e dependentes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "dB00tGUXyG84",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X = df.iloc[:, :37].values\n",
-        "y = df.iloc[:, -1].values.reshape(-1,1)\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "GHvWZhwryHjv",
-        "colab_type": "text"
-      },
-      "source": [
-        "Dividindo o dataset em conjunto de treinamento e testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Li7E8Hz4yHuq",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hIH9DERgyHH3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Criando o dicionário contendo todos os regressores"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "eNFfJ7XKyGU1",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "regressors = {'Linear Regression': LinearRegression(),\n",
-        "              'Decision Tree Reg:': DecisionTreeRegressor(random_state = 0),\n",
-        "              'Random Forest Reg': RandomForestRegressor(n_estimators = 10, random_state = 0),\n",
-        "              'SVR:': SVR(kernel = 'rbf')}"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Y2v9PGGqyhYX",
-        "colab_type": "text"
-      },
-      "source": [
-        "Criando dataframe que irá guardar os resultados finais dos regressores"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Dh5XDN5eyhgs",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df_results = pd.DataFrame(columns=['reg', 'rmse', 'rmse_log', 'r_2_score'])"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JliIuH-NylaA",
-        "colab_type": "text"
-      },
-      "source": [
-        "Percorrendo o dicionário e treinando e avaliando os modelos:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "1dRjpg9Mylkg",
-        "colab_type": "code",
-        "outputId": "4ce690cd-27c7-472b-9dc7-039d9149fec8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 139
-        }
-      },
-      "source": [
-        "for name, reg in regressors.items():\n",
-        "    \n",
-        "    # Treinando os regressores com Conjunto de Treinamento\n",
-        "    reg.fit(X_train, y_train)\n",
-        "    \n",
-        "    # Prevendo os resultados com o conjunto de testes\n",
-        "    y_pred = reg.predict(X_test)\n",
-        "    \n",
-        "    df_results.loc[len(df_results), :] = [name, reg.score(X_test, y_test), \n",
-        "                   mean_squared_error(y_test, y_pred), mean_squared_log_error(y_test, y_pred)]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
-            "  after removing the cwd from sys.path.\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-            "  y = column_or_1d(y, warn=True)\n",
-            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
-            "  \"avoid this warning.\", FutureWarning)\n"
-          ],
-          "name": "stderr"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jH-rHO9Cy0je",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibindo os resultados:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "3kYKXzOHy0uG",
-        "colab_type": "code",
-        "outputId": "413713e5-0553-4a11-9ba5-63156b66b564",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 173
-        }
-      },
-      "source": [
-        "df_results"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>reg</th>\n",
-              "      <th>rmse</th>\n",
-              "      <th>rmse_log</th>\n",
-              "      <th>r_2_score</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Linear Regression</td>\n",
-              "      <td>0.822678</td>\n",
-              "      <td>1.36012e+09</td>\n",
-              "      <td>0.0358379</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Decision Tree Reg:</td>\n",
-              "      <td>0.790632</td>\n",
-              "      <td>1.60592e+09</td>\n",
-              "      <td>0.0453292</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Random Forest Reg</td>\n",
-              "      <td>0.879513</td>\n",
-              "      <td>9.24177e+08</td>\n",
-              "      <td>0.0289542</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>SVR:</td>\n",
-              "      <td>-0.0249732</td>\n",
-              "      <td>7.86188e+09</td>\n",
-              "      <td>0.186869</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                  reg       rmse     rmse_log  r_2_score\n",
-              "0   Linear Regression   0.822678  1.36012e+09  0.0358379\n",
-              "1  Decision Tree Reg:   0.790632  1.60592e+09  0.0453292\n",
-              "2   Random Forest Reg   0.879513  9.24177e+08  0.0289542\n",
-              "3                SVR: -0.0249732  7.86188e+09   0.186869"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 65
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From a24c8e7ba900d5df66c021bd3f3917708565e25e Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 01:47:58 -0300
Subject: [PATCH 03/14] Created using Colaboratory

---
 .../treinamento_testes_validacao.ipynb        | 1071 +++++++++++++++++
 1 file changed, 1071 insertions(+)
 create mode 100644 notebooks/1_introducao/treinamento_testes_validacao.ipynb

diff --git a/notebooks/1_introducao/treinamento_testes_validacao.ipynb b/notebooks/1_introducao/treinamento_testes_validacao.ipynb
new file mode 100644
index 0000000..1711f87
--- /dev/null
+++ b/notebooks/1_introducao/treinamento_testes_validacao.ipynb
@@ -0,0 +1,1071 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "treinamento_testes_validacao.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/1_introducao/treinamento_testes_validacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w-RUHj0kHSFa",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Semana 1 - Aprendizagem Supervisonada\n",
+        "---\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YfzjpU8DEBKk",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Divisão dos dados e Avaliação"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fKb9Z7ynHEjA",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando as bibliotecas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ml7cyyo3abml",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.model_selection import KFold\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "74OGEe-tHc9r",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando os dados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XhtrtAb9EvT7",
+        "colab_type": "code",
+        "outputId": "530fc497-50e0-4ef4-c35d-9fba49237d55",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        }
+      },
+      "source": [
+        "data_url = 'https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/preprocessing_data.csv'\n",
+        "\n",
+        "df = pd.read_csv(data_url)\n",
+        "\n",
+        "df.head(10)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Salary</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Acquired</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Paraiba</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>6500.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>4300.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Brasilia</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>4900.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Recife</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>5400.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Maceio</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>5200.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Belo Horizonte</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4700.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>7100.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Paraiba</td>\n",
+              "      <td>51.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Fortaleza</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>6000.0</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             City   Age  Salary Sex Acquired\n",
+              "0         Paraiba  42.0  6500.0   M       No\n",
+              "1       Sao Paulo  21.0  4300.0   F      Yes\n",
+              "2        Brasilia  28.0  4900.0   F       No\n",
+              "3          Recife  35.0  5400.0   F       No\n",
+              "4          Maceio  38.0     NaN   M      Yes\n",
+              "5       Sao Paulo  33.0  5200.0   M      Yes\n",
+              "6  Belo Horizonte   NaN  4700.0   F       No\n",
+              "7       Sao Paulo  45.0  7100.0   M      Yes\n",
+              "8         Paraiba  51.0  7500.0   F       No\n",
+              "9       Fortaleza  39.0  6000.0   M      Yes"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G2M6D7WTALj1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yNgIaT9EALvx",
+        "colab_type": "code",
+        "outputId": "6158244f-34de-4fa9-93b4-21621af2ae27",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        }
+      },
+      "source": [
+        "df.info()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 10 entries, 0 to 9\n",
+            "Data columns (total 5 columns):\n",
+            "City        10 non-null object\n",
+            "Age         9 non-null float64\n",
+            "Salary      9 non-null float64\n",
+            "Sex         10 non-null object\n",
+            "Acquired    10 non-null object\n",
+            "dtypes: float64(2), object(3)\n",
+            "memory usage: 528.0+ bytes\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BLxBSWlxuPMs",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os registros nuḿericos que não possuem valores com a média."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "q5eiqJbywBLL",
+        "colab_type": "code",
+        "outputId": "7b8afeba-7dbc-48ae-8730-d8d97d722381",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        }
+      },
+      "source": [
+        "df = df.fillna(df.mean())\n",
+        "\n",
+        "df"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Salary</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Acquired</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Paraiba</td>\n",
+              "      <td>42.000000</td>\n",
+              "      <td>6500.000000</td>\n",
+              "      <td>M</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>4300.000000</td>\n",
+              "      <td>F</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Brasilia</td>\n",
+              "      <td>28.000000</td>\n",
+              "      <td>4900.000000</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Recife</td>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>5400.000000</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Maceio</td>\n",
+              "      <td>38.000000</td>\n",
+              "      <td>5733.333333</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>33.000000</td>\n",
+              "      <td>5200.000000</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Belo Horizonte</td>\n",
+              "      <td>36.888889</td>\n",
+              "      <td>4700.000000</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Sao Paulo</td>\n",
+              "      <td>45.000000</td>\n",
+              "      <td>7100.000000</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Paraiba</td>\n",
+              "      <td>51.000000</td>\n",
+              "      <td>7500.000000</td>\n",
+              "      <td>F</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Fortaleza</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>6000.000000</td>\n",
+              "      <td>M</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "             City        Age       Salary Sex Acquired\n",
+              "0         Paraiba  42.000000  6500.000000   M       No\n",
+              "1       Sao Paulo  21.000000  4300.000000   F      Yes\n",
+              "2        Brasilia  28.000000  4900.000000   F       No\n",
+              "3          Recife  35.000000  5400.000000   F       No\n",
+              "4          Maceio  38.000000  5733.333333   M      Yes\n",
+              "5       Sao Paulo  33.000000  5200.000000   M      Yes\n",
+              "6  Belo Horizonte  36.888889  4700.000000   F       No\n",
+              "7       Sao Paulo  45.000000  7100.000000   M      Yes\n",
+              "8         Paraiba  51.000000  7500.000000   F       No\n",
+              "9       Fortaleza  39.000000  6000.000000   M      Yes"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KosHK6EKEwIO",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindos as variáveis independentes (X) e dependente (y)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dyTqObUCEwQc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X = df.iloc[:, :-1].values"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9S0CTHw7kULG",
+        "colab_type": "code",
+        "outputId": "beecf767-293e-4922-a16a-013fc90016e8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "y = df.iloc[:, 4].values\n",
+        "y"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ecRq9DQdDlg6",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Efetividade na Classificação"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD4tGNnryE1V",
+        "colab_type": "text"
+      },
+      "source": [
+        "Criando as funções que calculam as diferentes métricas de efetividade:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "68pzp-TcE8IC"
+      },
+      "source": [
+        "### Cálculo das Medidas de Efetividade"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1-5WDv4uBsI9",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as funções de efetividade e importando bibliotecas:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-dAa8zihyEP6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função que calcula os reais positivos\n",
+        "def rp(tp, fn):\n",
+        "    return tp + fn\n",
+        "\n",
+        "# Função que calcula os reais negativos     \n",
+        "def rn(fp, tn):\n",
+        "    return fp + tn\n",
+        "\n",
+        "# Função que calcula as predições positivas  \n",
+        "def pp(tp, fp):\n",
+        "    return tp + fp\n",
+        "\n",
+        "# Função que calcula as predições negativas   \n",
+        "def pn(fn, tn):\n",
+        "    return fn + tn\n",
+        "\n",
+        "# Função que calcula Acurácia do modelo\n",
+        "def accuracy (tp, fp, fn, tn):\n",
+        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
+        "     return (accuracy)\n",
+        "    \n",
+        "# Função que calcula a Precisão \n",
+        "def precision (tp, fp):\n",
+        "    precision =  (tp / (tp + fp)) #predições positivas\n",
+        "    return precision\n",
+        "\n",
+        "# Função que calcula o Recall ou Taxa de True Positive (TTP)\n",
+        "def recall(tp, fn):\n",
+        "    recall =  (tp / (tp + fn)) # reais positivos\n",
+        "    return recall\n",
+        "\n",
+        "## Função que calcula o f-measure (media harmonica entre Precision e Recall)\n",
+        "def f_measure(tp, fp, fn):\n",
+        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
+        "    return f_measure\n",
+        "  \n",
+        "# Função que calcula o Informedness \n",
+        "def informedness(tp, fp, fn, tn):\n",
+        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
+        "    return inform\n",
+        "\n",
+        "# Função que calcula o Markedness\n",
+        "def markdness(tp, fp, fn, tn):    \n",
+        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
+        "    return mark\n",
+        "\n",
+        "# Função que calcula a taxa de False Positive (TFP)\n",
+        "def tfp(fp, tn):\n",
+        "  tfp = (fp / (tn + fp))\n",
+        "  return tfp"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nnfyAWe60hcM",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definido os valores da Matriz de Confusão"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WJOtHg660hnL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "tp, fp, fn, tn = [1,1,1,2]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cBnCTrcs0h75",
+        "colab_type": "text"
+      },
+      "source": [
+        "Calculando a Acurácia:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JrDXlhEp0iD_",
+        "colab_type": "code",
+        "outputId": "869cef36-b9f9-4c10-d0bd-723ac3b704be",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "accuracy (tp, fp, fn, tn)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.6"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S1EgduK_0iMN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Calculando a Precision:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aL86q9YP0iTa",
+        "colab_type": "code",
+        "outputId": "5a02aa2a-a423-4f23-8391-7dab6a1693dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "precision (tp, fp)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RISuvABl1QRM",
+        "colab_type": "text"
+      },
+      "source": [
+        "Calculando o Recall:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JHbboPE-1OBa",
+        "colab_type": "code",
+        "outputId": "cfb49698-1f83-4521-afbc-09931309e33b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        " recall(tp, fn)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D1Z-POVy1NzX",
+        "colab_type": "text"
+      },
+      "source": [
+        "Calculando o Informedness: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_wmrvULM1TiK",
+        "colab_type": "code",
+        "outputId": "6583e214-baa5-4a0a-a566-c92efb81b4b2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "informedness(tp, fp, fn, tn)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.16666666666666669"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SaQbezyD1OVs",
+        "colab_type": "text"
+      },
+      "source": [
+        "Calculando o Markedness:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LRJSJy_n1oLZ",
+        "colab_type": "code",
+        "outputId": "e246edb4-d9b9-4e24-f300-6299b83cba41",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "markdness(tp, fp, fn, tn)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.16666666666666669"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sOaWuY7dE84q",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Treinamento e Testes de Modelos de Predição"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PS0qgPh6ywSY",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Divisão do Dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D115SXB4GJGC",
+        "colab_type": "text"
+      },
+      "source": [
+        "Dividindo o dataset no conjunto de treinamento e testes. Normalmente o conjunto de testes equivale a 1/3 do dataset total:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QImAPykOGMOz",
+        "colab_type": "code",
+        "outputId": "5cc77020-2fb9-4beb-e3e3-af5847d36070",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        }
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 42)\n",
+        "\n",
+        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
+        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
+        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Tamanho do Dataset. 10\n",
+            "Tamanho do Conjunto de Treinamento. 6\n",
+            "Tamanho do Conjunto de Testes. 4\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bWSSELsxIpJz",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Validação Cruzada\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W_gdmirlF12c",
+        "colab_type": "text"
+      },
+      "source": [
+        "Criando uma função para definir os índices de uma validação cruzada:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zjwKGpKry2nv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função que retorna os indices do validação cruzada em k folds\n",
+        "def get_folds(indexes, k = 5, seed = 42):\n",
+        "    \n",
+        "    \"\"\"Função que retorna os indices do validação cruzada em k folds\n",
+        "\n",
+        "    Parâmetros\n",
+        "    ----------\n",
+        "    \n",
+        "    indexes : array\n",
+        "        Indices do dataframe\n",
+        "    \n",
+        "    k : int, optional, default=5\n",
+        "        Numero de folds. Deve ser pelo menos 2.\n",
+        "    \n",
+        "    seed : int, optional, default=42\n",
+        "        È o valor usado pelo gerador de números aleatórios;\n",
+        "     \"\"\" \n",
+        "        \n",
+        "    size = len(indexes)\n",
+        "    subset_size = round(size / k)\n",
+        "    random.Random(seed).shuffle(indexes)\n",
+        "    subsets = [indexes[x:x+subset_size] for x in range(0, len(indexes), subset_size)]\n",
+        "    kfolds = []\n",
+        "    for i in range(k):\n",
+        "        test = subsets[i]\n",
+        "        train = []\n",
+        "        for subset in subsets:\n",
+        "            if subset != test:\n",
+        "                train.extend(subset)\n",
+        "        kfolds.append((train, test))\n",
+        "    return kfolds\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ielGePYV0DoA",
+        "colab_type": "text"
+      },
+      "source": [
+        "Utilizando a função criada para definir os índices de treinamento e testes a serem utilizados usando 5 folds:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GFCur9iMslil",
+        "colab_type": "code",
+        "outputId": "ca374f2d-d8db-4186-9b6c-a92c2a7fa2ce",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "instances = list(df.index.values)\n",
+        "instances"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vQRhOlU3JOdY",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando a tupla com os índices da validação cruzada através da função criada:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UQzzYUAFJOmz",
+        "colab_type": "code",
+        "outputId": "9f512dd6-e941-4ac9-adb6-eb14c083f86d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "get_folds(list(df.index.values))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[([2, 8, 5, 6, 9, 4, 0, 1], [7, 3]),\n",
+              " ([7, 3, 5, 6, 9, 4, 0, 1], [2, 8]),\n",
+              " ([7, 3, 2, 8, 9, 4, 0, 1], [5, 6]),\n",
+              " ([7, 3, 2, 8, 5, 6, 0, 1], [9, 4]),\n",
+              " ([7, 3, 2, 8, 5, 6, 9, 4], [0, 1])]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FWvLV6ZjF2Ak",
+        "colab_type": "code",
+        "outputId": "f79ecfb3-34c8-4d4a-b20d-2828247fc05e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "# Pecorrendo a lista que contém as tuplas com os índices de treinamento e testes.\n",
+        "for fold in get_folds(list(df.index.values)):\n",
+        "     print(\"Indices de Treinamento:\", fold[0], \"Indices de Testes:\", fold[1])"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Indices de Treinamento: [2, 8, 5, 6, 9, 4, 0, 1] Indices de Testes: [7, 3]\n",
+            "Indices de Treinamento: [7, 3, 5, 6, 9, 4, 0, 1] Indices de Testes: [2, 8]\n",
+            "Indices de Treinamento: [7, 3, 2, 8, 9, 4, 0, 1] Indices de Testes: [5, 6]\n",
+            "Indices de Treinamento: [7, 3, 2, 8, 5, 6, 0, 1] Indices de Testes: [9, 4]\n",
+            "Indices de Treinamento: [7, 3, 2, 8, 5, 6, 9, 4] Indices de Testes: [0, 1]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 034ce1b9a5b19e072ffc1b6fb141e24c8b61582d Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 01:48:44 -0300
Subject: [PATCH 04/14] Delete treinamento_testes_validacao.ipynb

---
 .../treinamento_testes_validacao.ipynb        | 1071 -----------------
 1 file changed, 1071 deletions(-)
 delete mode 100644 notebooks/1_introducao/treinamento_testes_validacao.ipynb

diff --git a/notebooks/1_introducao/treinamento_testes_validacao.ipynb b/notebooks/1_introducao/treinamento_testes_validacao.ipynb
deleted file mode 100644
index 1711f87..0000000
--- a/notebooks/1_introducao/treinamento_testes_validacao.ipynb
+++ /dev/null
@@ -1,1071 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "treinamento_testes_validacao.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/1_introducao/treinamento_testes_validacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "w-RUHj0kHSFa",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Semana 1 - Aprendizagem Supervisonada\n",
-        "---\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "YfzjpU8DEBKk",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Divisão dos dados e Avaliação"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fKb9Z7ynHEjA",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando as bibliotecas"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ml7cyyo3abml",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.model_selection import KFold\n",
-        "\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "74OGEe-tHc9r",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando os dados"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "XhtrtAb9EvT7",
-        "colab_type": "code",
-        "outputId": "530fc497-50e0-4ef4-c35d-9fba49237d55",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 359
-        }
-      },
-      "source": [
-        "data_url = 'https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/preprocessing_data.csv'\n",
-        "\n",
-        "df = pd.read_csv(data_url)\n",
-        "\n",
-        "df.head(10)"
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>City</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Salary</th>\n",
-              "      <th>Sex</th>\n",
-              "      <th>Acquired</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Paraiba</td>\n",
-              "      <td>42.0</td>\n",
-              "      <td>6500.0</td>\n",
-              "      <td>M</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>21.0</td>\n",
-              "      <td>4300.0</td>\n",
-              "      <td>F</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Brasilia</td>\n",
-              "      <td>28.0</td>\n",
-              "      <td>4900.0</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Recife</td>\n",
-              "      <td>35.0</td>\n",
-              "      <td>5400.0</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Maceio</td>\n",
-              "      <td>38.0</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>33.0</td>\n",
-              "      <td>5200.0</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>Belo Horizonte</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>4700.0</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>45.0</td>\n",
-              "      <td>7100.0</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>Paraiba</td>\n",
-              "      <td>51.0</td>\n",
-              "      <td>7500.0</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>Fortaleza</td>\n",
-              "      <td>39.0</td>\n",
-              "      <td>6000.0</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "             City   Age  Salary Sex Acquired\n",
-              "0         Paraiba  42.0  6500.0   M       No\n",
-              "1       Sao Paulo  21.0  4300.0   F      Yes\n",
-              "2        Brasilia  28.0  4900.0   F       No\n",
-              "3          Recife  35.0  5400.0   F       No\n",
-              "4          Maceio  38.0     NaN   M      Yes\n",
-              "5       Sao Paulo  33.0  5200.0   M      Yes\n",
-              "6  Belo Horizonte   NaN  4700.0   F       No\n",
-              "7       Sao Paulo  45.0  7100.0   M      Yes\n",
-              "8         Paraiba  51.0  7500.0   F       No\n",
-              "9       Fortaleza  39.0  6000.0   M      Yes"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "G2M6D7WTALj1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "yNgIaT9EALvx",
-        "colab_type": "code",
-        "outputId": "6158244f-34de-4fa9-93b4-21621af2ae27",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 187
-        }
-      },
-      "source": [
-        "df.info()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "RangeIndex: 10 entries, 0 to 9\n",
-            "Data columns (total 5 columns):\n",
-            "City        10 non-null object\n",
-            "Age         9 non-null float64\n",
-            "Salary      9 non-null float64\n",
-            "Sex         10 non-null object\n",
-            "Acquired    10 non-null object\n",
-            "dtypes: float64(2), object(3)\n",
-            "memory usage: 528.0+ bytes\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BLxBSWlxuPMs",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os registros nuḿericos que não possuem valores com a média."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "q5eiqJbywBLL",
-        "colab_type": "code",
-        "outputId": "7b8afeba-7dbc-48ae-8730-d8d97d722381",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 359
-        }
-      },
-      "source": [
-        "df = df.fillna(df.mean())\n",
-        "\n",
-        "df"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>City</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>Salary</th>\n",
-              "      <th>Sex</th>\n",
-              "      <th>Acquired</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>Paraiba</td>\n",
-              "      <td>42.000000</td>\n",
-              "      <td>6500.000000</td>\n",
-              "      <td>M</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>21.000000</td>\n",
-              "      <td>4300.000000</td>\n",
-              "      <td>F</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>Brasilia</td>\n",
-              "      <td>28.000000</td>\n",
-              "      <td>4900.000000</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>Recife</td>\n",
-              "      <td>35.000000</td>\n",
-              "      <td>5400.000000</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>Maceio</td>\n",
-              "      <td>38.000000</td>\n",
-              "      <td>5733.333333</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>33.000000</td>\n",
-              "      <td>5200.000000</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6</th>\n",
-              "      <td>Belo Horizonte</td>\n",
-              "      <td>36.888889</td>\n",
-              "      <td>4700.000000</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7</th>\n",
-              "      <td>Sao Paulo</td>\n",
-              "      <td>45.000000</td>\n",
-              "      <td>7100.000000</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8</th>\n",
-              "      <td>Paraiba</td>\n",
-              "      <td>51.000000</td>\n",
-              "      <td>7500.000000</td>\n",
-              "      <td>F</td>\n",
-              "      <td>No</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9</th>\n",
-              "      <td>Fortaleza</td>\n",
-              "      <td>39.000000</td>\n",
-              "      <td>6000.000000</td>\n",
-              "      <td>M</td>\n",
-              "      <td>Yes</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "             City        Age       Salary Sex Acquired\n",
-              "0         Paraiba  42.000000  6500.000000   M       No\n",
-              "1       Sao Paulo  21.000000  4300.000000   F      Yes\n",
-              "2        Brasilia  28.000000  4900.000000   F       No\n",
-              "3          Recife  35.000000  5400.000000   F       No\n",
-              "4          Maceio  38.000000  5733.333333   M      Yes\n",
-              "5       Sao Paulo  33.000000  5200.000000   M      Yes\n",
-              "6  Belo Horizonte  36.888889  4700.000000   F       No\n",
-              "7       Sao Paulo  45.000000  7100.000000   M      Yes\n",
-              "8         Paraiba  51.000000  7500.000000   F       No\n",
-              "9       Fortaleza  39.000000  6000.000000   M      Yes"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "KosHK6EKEwIO",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindos as variáveis independentes (X) e dependente (y)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "dyTqObUCEwQc",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X = df.iloc[:, :-1].values"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "9S0CTHw7kULG",
-        "colab_type": "code",
-        "outputId": "beecf767-293e-4922-a16a-013fc90016e8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "y = df.iloc[:, 4].values\n",
-        "y"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['No', 'Yes', 'No', 'No', 'Yes', 'Yes', 'No', 'Yes', 'No', 'Yes'],\n",
-              "      dtype=object)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ecRq9DQdDlg6",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Efetividade na Classificação"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zD4tGNnryE1V",
-        "colab_type": "text"
-      },
-      "source": [
-        "Criando as funções que calculam as diferentes métricas de efetividade:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "68pzp-TcE8IC"
-      },
-      "source": [
-        "### Cálculo das Medidas de Efetividade"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1-5WDv4uBsI9",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as funções de efetividade e importando bibliotecas:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "-dAa8zihyEP6",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função que calcula os reais positivos\n",
-        "def rp(tp, fn):\n",
-        "    return tp + fn\n",
-        "\n",
-        "# Função que calcula os reais negativos     \n",
-        "def rn(fp, tn):\n",
-        "    return fp + tn\n",
-        "\n",
-        "# Função que calcula as predições positivas  \n",
-        "def pp(tp, fp):\n",
-        "    return tp + fp\n",
-        "\n",
-        "# Função que calcula as predições negativas   \n",
-        "def pn(fn, tn):\n",
-        "    return fn + tn\n",
-        "\n",
-        "# Função que calcula Acurácia do modelo\n",
-        "def accuracy (tp, fp, fn, tn):\n",
-        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
-        "     return (accuracy)\n",
-        "    \n",
-        "# Função que calcula a Precisão \n",
-        "def precision (tp, fp):\n",
-        "    precision =  (tp / (tp + fp)) #predições positivas\n",
-        "    return precision\n",
-        "\n",
-        "# Função que calcula o Recall ou Taxa de True Positive (TTP)\n",
-        "def recall(tp, fn):\n",
-        "    recall =  (tp / (tp + fn)) # reais positivos\n",
-        "    return recall\n",
-        "\n",
-        "## Função que calcula o f-measure (media harmonica entre Precision e Recall)\n",
-        "def f_measure(tp, fp, fn):\n",
-        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
-        "    return f_measure\n",
-        "  \n",
-        "# Função que calcula o Informedness \n",
-        "def informedness(tp, fp, fn, tn):\n",
-        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
-        "    return inform\n",
-        "\n",
-        "# Função que calcula o Markedness\n",
-        "def markdness(tp, fp, fn, tn):    \n",
-        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
-        "    return mark\n",
-        "\n",
-        "# Função que calcula a taxa de False Positive (TFP)\n",
-        "def tfp(fp, tn):\n",
-        "  tfp = (fp / (tn + fp))\n",
-        "  return tfp"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nnfyAWe60hcM",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definido os valores da Matriz de Confusão"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "WJOtHg660hnL",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "tp, fp, fn, tn = [1,1,1,2]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cBnCTrcs0h75",
-        "colab_type": "text"
-      },
-      "source": [
-        "Calculando a Acurácia:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "JrDXlhEp0iD_",
-        "colab_type": "code",
-        "outputId": "869cef36-b9f9-4c10-d0bd-723ac3b704be",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "accuracy (tp, fp, fn, tn)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.6"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "S1EgduK_0iMN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Calculando a Precision:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aL86q9YP0iTa",
-        "colab_type": "code",
-        "outputId": "5a02aa2a-a423-4f23-8391-7dab6a1693dd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "precision (tp, fp)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "RISuvABl1QRM",
-        "colab_type": "text"
-      },
-      "source": [
-        "Calculando o Recall:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "JHbboPE-1OBa",
-        "colab_type": "code",
-        "outputId": "cfb49698-1f83-4521-afbc-09931309e33b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        " recall(tp, fn)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "D1Z-POVy1NzX",
-        "colab_type": "text"
-      },
-      "source": [
-        "Calculando o Informedness: "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "_wmrvULM1TiK",
-        "colab_type": "code",
-        "outputId": "6583e214-baa5-4a0a-a566-c92efb81b4b2",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "informedness(tp, fp, fn, tn)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.16666666666666669"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SaQbezyD1OVs",
-        "colab_type": "text"
-      },
-      "source": [
-        "Calculando o Markedness:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "LRJSJy_n1oLZ",
-        "colab_type": "code",
-        "outputId": "e246edb4-d9b9-4e24-f300-6299b83cba41",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "markdness(tp, fp, fn, tn)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.16666666666666669"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sOaWuY7dE84q",
-        "colab_type": "text"
-      },
-      "source": [
-        "## Treinamento e Testes de Modelos de Predição"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "PS0qgPh6ywSY",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Divisão do Dataset"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "D115SXB4GJGC",
-        "colab_type": "text"
-      },
-      "source": [
-        "Dividindo o dataset no conjunto de treinamento e testes. Normalmente o conjunto de testes equivale a 1/3 do dataset total:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "QImAPykOGMOz",
-        "colab_type": "code",
-        "outputId": "5cc77020-2fb9-4beb-e3e3-af5847d36070",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 68
-        }
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 42)\n",
-        "\n",
-        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
-        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
-        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Tamanho do Dataset. 10\n",
-            "Tamanho do Conjunto de Treinamento. 6\n",
-            "Tamanho do Conjunto de Testes. 4\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bWSSELsxIpJz",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Validação Cruzada\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "W_gdmirlF12c",
-        "colab_type": "text"
-      },
-      "source": [
-        "Criando uma função para definir os índices de uma validação cruzada:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "zjwKGpKry2nv",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função que retorna os indices do validação cruzada em k folds\n",
-        "def get_folds(indexes, k = 5, seed = 42):\n",
-        "    \n",
-        "    \"\"\"Função que retorna os indices do validação cruzada em k folds\n",
-        "\n",
-        "    Parâmetros\n",
-        "    ----------\n",
-        "    \n",
-        "    indexes : array\n",
-        "        Indices do dataframe\n",
-        "    \n",
-        "    k : int, optional, default=5\n",
-        "        Numero de folds. Deve ser pelo menos 2.\n",
-        "    \n",
-        "    seed : int, optional, default=42\n",
-        "        È o valor usado pelo gerador de números aleatórios;\n",
-        "     \"\"\" \n",
-        "        \n",
-        "    size = len(indexes)\n",
-        "    subset_size = round(size / k)\n",
-        "    random.Random(seed).shuffle(indexes)\n",
-        "    subsets = [indexes[x:x+subset_size] for x in range(0, len(indexes), subset_size)]\n",
-        "    kfolds = []\n",
-        "    for i in range(k):\n",
-        "        test = subsets[i]\n",
-        "        train = []\n",
-        "        for subset in subsets:\n",
-        "            if subset != test:\n",
-        "                train.extend(subset)\n",
-        "        kfolds.append((train, test))\n",
-        "    return kfolds\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ielGePYV0DoA",
-        "colab_type": "text"
-      },
-      "source": [
-        "Utilizando a função criada para definir os índices de treinamento e testes a serem utilizados usando 5 folds:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "GFCur9iMslil",
-        "colab_type": "code",
-        "outputId": "ca374f2d-d8db-4186-9b6c-a92c2a7fa2ce",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "instances = list(df.index.values)\n",
-        "instances"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 22
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "vQRhOlU3JOdY",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando a tupla com os índices da validação cruzada através da função criada:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "UQzzYUAFJOmz",
-        "colab_type": "code",
-        "outputId": "9f512dd6-e941-4ac9-adb6-eb14c083f86d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "get_folds(list(df.index.values))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[([2, 8, 5, 6, 9, 4, 0, 1], [7, 3]),\n",
-              " ([7, 3, 5, 6, 9, 4, 0, 1], [2, 8]),\n",
-              " ([7, 3, 2, 8, 9, 4, 0, 1], [5, 6]),\n",
-              " ([7, 3, 2, 8, 5, 6, 0, 1], [9, 4]),\n",
-              " ([7, 3, 2, 8, 5, 6, 9, 4], [0, 1])]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FWvLV6ZjF2Ak",
-        "colab_type": "code",
-        "outputId": "f79ecfb3-34c8-4d4a-b20d-2828247fc05e",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "# Pecorrendo a lista que contém as tuplas com os índices de treinamento e testes.\n",
-        "for fold in get_folds(list(df.index.values)):\n",
-        "     print(\"Indices de Treinamento:\", fold[0], \"Indices de Testes:\", fold[1])"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Indices de Treinamento: [2, 8, 5, 6, 9, 4, 0, 1] Indices de Testes: [7, 3]\n",
-            "Indices de Treinamento: [7, 3, 5, 6, 9, 4, 0, 1] Indices de Testes: [2, 8]\n",
-            "Indices de Treinamento: [7, 3, 2, 8, 9, 4, 0, 1] Indices de Testes: [5, 6]\n",
-            "Indices de Treinamento: [7, 3, 2, 8, 5, 6, 0, 1] Indices de Testes: [9, 4]\n",
-            "Indices de Treinamento: [7, 3, 2, 8, 5, 6, 9, 4] Indices de Testes: [0, 1]\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 5ce95734013632cff11ea265acf15e94502122dd Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:05:44 -0300
Subject: [PATCH 05/14] Created using Colaboratory

---
 notebooks/2_semana/Regressao_Logistica.ipynb | 1287 ++++++++++++++++++
 1 file changed, 1287 insertions(+)
 create mode 100644 notebooks/2_semana/Regressao_Logistica.ipynb

diff --git a/notebooks/2_semana/Regressao_Logistica.ipynb b/notebooks/2_semana/Regressao_Logistica.ipynb
new file mode 100644
index 0000000..81ecf4f
--- /dev/null
+++ b/notebooks/2_semana/Regressao_Logistica.ipynb
@@ -0,0 +1,1287 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Regressao_Logistica.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "0KkcOxCxjptF",
+        "8CLZyvMlh_Qj",
+        "gIqtlsYGmOYq"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Logistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "#Modelos de Classificação : Regressão Logística"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0KkcOxCxjptF",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs  e funções"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Importando libs de visualização de dados\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns; sns.set()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função que calcula os reais positivos\n",
+        "def rp(tp, fn):\n",
+        "    return tp + fn\n",
+        "\n",
+        "# Função que calcula os reais negativos     \n",
+        "def rn(fp, tn):\n",
+        "    return fp + tn\n",
+        "\n",
+        "# Função que calcula as predicoes positivas  \n",
+        "def pp(tp, fp):\n",
+        "    return tp + fp\n",
+        "\n",
+        "# Função que calcula as predicoes negativas   \n",
+        "def pn(fn, tn):\n",
+        "    return fn + tn\n",
+        "\n",
+        "# Função que calcula acurácia do modelo\n",
+        "def accuracy (tp, fp, fn, tn):\n",
+        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
+        "     return (accuracy)\n",
+        "    \n",
+        "# Função que calcula a precisão \n",
+        "def precision (tp, fp):\n",
+        "    precision =  (tp / (tp + fp)) #predições positivas\n",
+        "    return precision\n",
+        "\n",
+        "# Função que calcula o recall\n",
+        "def recall(tp, fn):\n",
+        "    recall =  (tp / (tp + fn)) # reais positivos\n",
+        "    return recall\n",
+        "\n",
+        "## Função que calcula o f-measure (media harmonica entre precision e recall)\n",
+        "def f_measure(tp, fp, fn):\n",
+        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
+        "    return f_measure\n",
+        "  \n",
+        "# Função que calcula o Informedness \n",
+        "def informedness(tp, fp, fn, tn):\n",
+        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
+        "    return inform\n",
+        "\n",
+        "# Função que calcula o Markedness\n",
+        "def markdness(tp, fp, fn, tn):    \n",
+        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
+        "    return mark\n",
+        "\n",
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de classificação\n",
+        "def plot_results_class(X, y, classifier, title):\n",
+        "    X_set, y_set = X, y\n",
+        "    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
+        "                         np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
+        "    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
+        "                 alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
+        "    plt.xlim(X1.min(), X1.max())\n",
+        "    plt.ylim(X2.min(), X2.max())\n",
+        "    for i, j in enumerate(np.unique(y_set)):\n",
+        "        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
+        "                    c = ListedColormap(('red', 'green'))(i), label = j)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Idade')\n",
+        "    plt.ylabel('Tarifa')\n",
+        "    plt.legend()\n",
+        "    plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8CLZyvMlh_Qj",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos dados"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando os dados dos passageiros do titanic e selecionando uma amostragem dos dados para uma melhor visualização. O objetivo dessa tarefa de classificação é prever os passageiros sobreviventes com base nas suas informações pessoais.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/titanic/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
+        "df = df.sample(n=100, random_state=0, replace = True)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "6f217547-c6ba-4f14-ae3a-da80b4096355",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        }
+      },
+      "source": [
+        "# Exporando o dataset\n",
+        "df.info()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 100 entries, 44 to 20\n",
+            "Data columns (total 8 columns):\n",
+            "Unnamed: 0     100 non-null int64\n",
+            "LotArea        100 non-null int64\n",
+            "PoolArea       100 non-null int64\n",
+            "GarageArea     100 non-null int64\n",
+            "OverallCond    100 non-null int64\n",
+            "YearBuilt      100 non-null int64\n",
+            "MSZoning       100 non-null object\n",
+            "SalePrice      100 non-null int64\n",
+            "dtypes: int64(7), object(1)\n",
+            "memory usage: 7.0+ KB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HQOuXFFIi8OZ",
+        "colab_type": "code",
+        "outputId": "d621591a-6226-4107-8eca-39e26ee223c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "# Visualizando o sumário das colunas numéricas do dataset\n",
+        "df.describe()"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>775.810000</td>\n",
+              "      <td>10898.090000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>522.930000</td>\n",
+              "      <td>5.890000</td>\n",
+              "      <td>1970.320000</td>\n",
+              "      <td>199800.560000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>394.243674</td>\n",
+              "      <td>6259.640208</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>233.049232</td>\n",
+              "      <td>1.301863</td>\n",
+              "      <td>31.138297</td>\n",
+              "      <td>76041.565893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>546.750000</td>\n",
+              "      <td>8381.250000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.000000</td>\n",
+              "      <td>148375.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>802.000000</td>\n",
+              "      <td>10760.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>551.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.000000</td>\n",
+              "      <td>195000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1039.000000</td>\n",
+              "      <td>12068.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>616.500000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1442.000000</td>\n",
+              "      <td>46589.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        Unnamed: 0       LotArea  ...    YearBuilt      SalePrice\n",
+              "count   100.000000    100.000000  ...   100.000000     100.000000\n",
+              "mean    775.810000  10898.090000  ...  1970.320000  199800.560000\n",
+              "std     394.243674   6259.640208  ...    31.138297   76041.565893\n",
+              "min      26.000000   1477.000000  ...  1875.000000   80000.000000\n",
+              "25%     546.750000   8381.250000  ...  1950.000000  148375.000000\n",
+              "50%     802.000000  10760.500000  ...  1976.000000  195000.000000\n",
+              "75%    1039.000000  12068.750000  ...  2002.000000  235032.000000\n",
+              "max    1442.000000  46589.000000  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 7 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "fb164251-d4c9-4c14-e780-bde3b35799f8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>496</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Yousseff, Mr. Gerious</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2627</td>\n",
+              "      <td>14.4583</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>649</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Willey, Mr. Edward</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>S.O./P.P. 751</td>\n",
+              "      <td>7.5500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>278</th>\n",
+              "      <td>279</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Rice, Master. Eric</td>\n",
+              "      <td>male</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>382652</td>\n",
+              "      <td>29.1250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17569</td>\n",
+              "      <td>146.5208</td>\n",
+              "      <td>B78</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>255</th>\n",
+              "      <td>256</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Touma, Mrs. Darwis (Hanne Youssef Razi)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2650</td>\n",
+              "      <td>15.2458</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     PassengerId  Survived  Pclass  ...      Fare Cabin  Embarked\n",
+              "495          496         0       3  ...   14.4583   NaN         C\n",
+              "648          649         0       3  ...    7.5500   NaN         S\n",
+              "278          279         0       3  ...   29.1250   NaN         Q\n",
+              "31            32         1       1  ...  146.5208   B78         C\n",
+              "255          256         1       3  ...   15.2458   NaN         C\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "705d845a-fa8d-4291-9e72-41ce575bf8ee",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>496</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Yousseff, Mr. Gerious</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2627</td>\n",
+              "      <td>14.4583</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>649</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Willey, Mr. Edward</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>S.O./P.P. 751</td>\n",
+              "      <td>7.5500</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>278</th>\n",
+              "      <td>279</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Rice, Master. Eric</td>\n",
+              "      <td>male</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>382652</td>\n",
+              "      <td>29.1250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>32</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17569</td>\n",
+              "      <td>146.5208</td>\n",
+              "      <td>B78</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>255</th>\n",
+              "      <td>256</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Touma, Mrs. Darwis (Hanne Youssef Razi)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2650</td>\n",
+              "      <td>15.2458</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     PassengerId  Survived  Pclass  ...      Fare Cabin  Embarked\n",
+              "495          496         0       3  ...   14.4583   NaN         C\n",
+              "648          649         0       3  ...    7.5500   NaN         S\n",
+              "278          279         0       3  ...   29.1250   NaN         Q\n",
+              "31            32         1       1  ...  146.5208   B78         C\n",
+              "255          256         1       3  ...   15.2458   NaN         C\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes e visualizando as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "0187a2d8-9a56-42f8-f315-176ec59a1219",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X = df.iloc[:, [5, 9]].values\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 26.    ,  14.4583],\n",
+              "       [ 26.    ,   7.55  ],\n",
+              "       [  7.    ,  29.125 ],\n",
+              "       [ 26.    , 146.5208],\n",
+              "       [ 29.    ,  15.2458]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes e visualizando as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "42cd730b-3886-48a1-e117-54da7663f8db",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "y = df.iloc[:, 1].values\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 0, 1, 1])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yzJ08rJ4J3CS",
+        "colab_type": "text"
+      },
+      "source": [
+        " Criando os subconjuntos de treinamento e testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tVz1mTUOJ4Tb",
+        "colab_type": "code",
+        "outputId": "c97d6fa7-10ea-4bec-c1d6-a67ad0a0b8c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 42)\n",
+        "\n",
+        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
+        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
+        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Tamanho do Dataset. 100\n",
+            "Tamanho do Conjunto de Treinamento. 80\n",
+            "Tamanho do Conjunto de Testes. 20\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features e exibindo as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "outputId": "1bbd577a-6f18-4d0b-c924-4f4d0baa371e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X_train = feature_scaling(X_train)\n",
+        "X_test = feature_scaling(X_test)\n",
+        "\n",
+        "X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.2233842 ,  0.62589282],\n",
+              "       [ 0.50330481, -0.63776468],\n",
+              "       [ 0.35796701, -0.68999987],\n",
+              "       [-0.0780464 , -0.55753246],\n",
+              "       [-0.58672871, -0.2466326 ]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jGXuG35WJ_Tj",
+        "colab_type": "text"
+      },
+      "source": [
+        "Treinando o modelo de Árvore de Decisão com o Conjunto de Treinamento"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "gIqtlsYGmOYq"
+      },
+      "source": [
+        "### Etapa de Treinamento e Validação do Modelo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "oF9z-OwymOYs"
+      },
+      "source": [
+        "Importando e treinando o modelo com o conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "8d5f3b06-3372-42a4-fe53-7affbad191ba",
+        "id": "G1uuhHzumOYt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "classifier = LogisticRegression(random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+            "  FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
+              "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
+              "                   warm_start=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "sKGRDMbWmOY1"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "10be0e36-54f6-4530-8378-9c4b2f72ff8e",
+        "id": "FAvYq8NemOY3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "2qVdkDyzmOY_"
+      },
+      "source": [
+        "Criando e exibindo os valores da matriz de confusão com o conjunto de testes "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "412d2fd2-d655-4a5c-c8c0-b906049447ec",
+        "id": "dzm-N1R7mOZD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
+        "\n",
+        "confusion_matrix(y_test, y_pred)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[14,  1],\n",
+              "       [ 3,  2]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "Vl2JrA-cmOZI"
+      },
+      "source": [
+        "Visualizando a matriz de confusão"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "b66d506b-fa33-4666-a495-c5587d614bf7",
+        "id": "o8x_wA2CmOZJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        }
+      },
+      "source": [
+        "mat = confusion_matrix(y_test, y_pred)\n",
+        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
+        "plt.xlabel('true label')\n",
+        "plt.ylabel('predicted label');"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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92rB9lfpm93Y6VosW5XQAhF5FpVfz5r+o/v36qk2b1k7HAYIio6JUWV6loYP+TeWfVSqr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\nBADAYJQAABiMEgAAg1ECAGCw/wOL4LBa8GRKHAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "y6TAt9MomOZL"
+      },
+      "source": [
+        "Visualizando a métrica de **acurácia** utilizando as funções criadas e a bibilioteca sklearn:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "680fcd92-32f2-4554-b847-0b75b5942e39",
+        "id": "rMMCqczomOZM",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "accuracy(tp, fp, fn, tn)\n",
+        "classifier.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "rAL1ztqJmOZP"
+      },
+      "source": [
+        "Exibindo a medida **f-measure**:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "60cff7ee-505a-4dc2-eb59-73785668ef2f",
+        "id": "dcg2oWJImOZQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "f_measure(tp, fp, fn)\n",
+        "f1_score(y_test, y_pred)  "
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "6SG8SzeXmOZS"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "8e82d219-2e85-4201-a88e-b4cbecb69c00",
+        "id": "_xENnpTxmOZU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "source": [
+        "plot_results_class(X_train, y_train, classifier, 'Regressão Logística (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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tuYJDPYcXX3wRjRs3BgAsWrQIxcXFuHr1Kl5++eUaDY7IUdU9IHpSSYrTackV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\ncgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "QwmncO8mmOZa"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "62772bbd-82e5-4f3a-fe3c-2f1f92430914",
+        "id": "tA-DeHAMmOZb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "source": [
+        "plot_results_class(X_test, y_test, classifier, 'Regressão Logística (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RmjRpAgBwcnJCcXExWrVqhcuXL1u1OCIikkeF1hyef/55XL58Ge3bt0fbtm0R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\nAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 575c0d8c9df5bede3e9933fcc59c16aec7a03ada Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:05:55 -0300
Subject: [PATCH 06/14] Created using Colaboratory

---
 notebooks/2_semana/Regressao_Multipla.ipynb | 1219 +++++++++++++++++++
 1 file changed, 1219 insertions(+)
 create mode 100644 notebooks/2_semana/Regressao_Multipla.ipynb

diff --git a/notebooks/2_semana/Regressao_Multipla.ipynb b/notebooks/2_semana/Regressao_Multipla.ipynb
new file mode 100644
index 0000000..093cfa3
--- /dev/null
+++ b/notebooks/2_semana/Regressao_Multipla.ipynb
@@ -0,0 +1,1219 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Regressao_Multipla.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "TsV0SiuwpZ7i",
+        "QaGBc_KwpeVt",
+        "VUZAwJjMvh0r"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Multipla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Modelos de Regressão: Regressão Linear Múltipla"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TsV0SiuwpZ7i",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs e funções:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regressão\n",
+        "def plot_results_linear(X, y, regressor, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
+        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()    \n",
+        "    \n",
+        "# Função que gera o gráfico dos resultados de arvores\n",
+        "def plot_results_reg(X, y, regressor, title):     \n",
+        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
+        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "     "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QaGBc_KwpeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos **dados**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
+        "\n",
+        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "bcace5e2-035f-4e8b-ed04-d21036a25367",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "df.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>11633.933333</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>504.683333</td>\n",
+              "      <td>5.783333</td>\n",
+              "      <td>1972.116667</td>\n",
+              "      <td>197536.150000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8099.879028</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>222.427554</td>\n",
+              "      <td>1.180228</td>\n",
+              "      <td>30.522618</td>\n",
+              "      <td>74910.507145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>8068.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.750000</td>\n",
+              "      <td>142250.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10510.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>542.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.500000</td>\n",
+              "      <td>188750.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12010.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>607.250000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>53227.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
+              "count     60.000000      60.0  ...    60.000000      60.000000\n",
+              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
+              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
+              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
+              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
+              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
+              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
+              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "b06300d2-add2-4c00-f192-52a2e732edf8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 237
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "11e16af0-7111-42a8-fa5a-b813693d64df",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 238
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "fc0ba041-626e-403f-f36e-a0b3fbfe3415",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 8450],\n",
+              "       [ 9600],\n",
+              "       [11250],\n",
+              "       [ 9550],\n",
+              "       [14260]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 239
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "d7c38a52-ee55-478a-9f50-af39542fe538",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[208500],\n",
+              "       [181500],\n",
+              "       [223500],\n",
+              "       [140000],\n",
+              "       [250000]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 240
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "VUZAwJjMvh0r"
+      },
+      "source": [
+        "###  Etapa de Classificação e Validação do Modelo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZOIXAi6Wvpce",
+        "colab_type": "text"
+      },
+      "source": [
+        "Selecionando as principais features do dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "et8CNkI1vpk2",
+        "colab_type": "code",
+        "outputId": "5b82c6a2-0346-4de7-f897-68ec246fd933",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].reindex()\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 241
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZjC2LJYh1SQ6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
+        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SeWh_rpnYkLd",
+        "colab_type": "code",
+        "outputId": "58ec4049-ab3a-4c72-be4c-921620731399",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "df['MSZoning'].unique()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 242
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eumBvAB8wbq-",
+        "colab_type": "text"
+      },
+      "source": [
+        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rjT1ONG7wb9x",
+        "colab_type": "code",
+        "outputId": "cd492757-3b55-42bc-feb6-43de14ff9341",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "      <th>MSZoning_FV</th>\n",
+              "      <th>MSZoning_RH</th>\n",
+              "      <th>MSZoning_RL</th>\n",
+              "      <th>MSZoning_RM</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>208500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>181500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>223500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>140000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>250000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
+              "0     8450         0         548  ...            0            1            0\n",
+              "1     9600         0         460  ...            0            1            0\n",
+              "2    11250         0         608  ...            0            1            0\n",
+              "3     9550         0         642  ...            0            1            0\n",
+              "4    14260         0         836  ...            0            1            0\n",
+              "\n",
+              "[5 rows x 10 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 243
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i9fs5x8MvpxV",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b9uRFW2vvp6b",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
+        "y = df['SalePrice'].values.reshape(-1,1)\n",
+        "\n",
+        "# Dividindo os dados\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "outputId": "8fa737f1-578b-4644-de0f-b1ee66fcc7d0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        }
+      },
+      "source": [
+        "X_train = feature_scaling(X_train)\n",
+        "X_test = feature_scaling(X_test)\n",
+        "\n",
+        "X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.21289571, -0.07099284, -0.86383727,  0.3722173 , -0.45546896,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.26524463, -0.07099284, -0.45626397,  1.26860866,  0.71860895,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.17784146, -0.07099284, -2.25716927,  1.26860866, -1.98829291,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.32447422, -0.07099284, -1.11975539,  1.26860866, -1.10773447,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.52903482, -0.07099284, -0.79748813,  0.3722173 , -1.53170705,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 245
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "SMaN0iHNvh0t"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "0ce99d9c-c744-4034-fec4-7cdc01565225",
+        "id": "VuzaRxyuvh0u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 246
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "zT6PUXkWvh0y"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "1da72340-52a8-4022-a2e5-363eb7f76251",
+        "id": "JR-E180Fvh0y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[162102.94940288],\n",
+              "       [241746.81922749],\n",
+              "       [119733.72469521],\n",
+              "       [147800.757112  ],\n",
+              "       [285853.71056469]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 247
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "PO6sGI3Gvh01"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "6f12ba33-16a7-4559-de51-fb0201398258",
+        "id": "fQPgnd8dvh02",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5043465841755175"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 248
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From b908b7cdc19d44fe6a074f2fc46923de2f21ccd6 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:06:08 -0300
Subject: [PATCH 07/14] Created using Colaboratory

---
 .../Regress\303\243o_Polynomial.ipynb"        | 1087 +++++++++++++++++
 1 file changed, 1087 insertions(+)
 create mode 100644 "notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"

diff --git "a/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb" "b/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"
new file mode 100644
index 0000000..8e0d84d
--- /dev/null
+++ "b/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"
@@ -0,0 +1,1087 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Regressão_Polynomial.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "TsV0SiuwpZ7i",
+        "QaGBc_KwpeVt",
+        "hDpjk2hPqBar"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regress%C3%A3o_Polynomial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Modelos de Regressão: Regressão Polynomial"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TsV0SiuwpZ7i",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs e funções:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regressão\n",
+        "def plot_results_linear(X, y, regressor, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
+        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()    \n",
+        "    \n",
+        "# Função que gera o gráfico dos resultados de arvores\n",
+        "def plot_results_reg(X, y, regressor, title):     \n",
+        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
+        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "     "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QaGBc_KwpeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos **dados**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
+        "\n",
+        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "68b7c2d6-e1ce-4fbd-fcce-e66c30090c23",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "df.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>11633.933333</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>504.683333</td>\n",
+              "      <td>5.783333</td>\n",
+              "      <td>1972.116667</td>\n",
+              "      <td>197536.150000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8099.879028</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>222.427554</td>\n",
+              "      <td>1.180228</td>\n",
+              "      <td>30.522618</td>\n",
+              "      <td>74910.507145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>8068.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.750000</td>\n",
+              "      <td>142250.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10510.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>542.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.500000</td>\n",
+              "      <td>188750.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12010.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>607.250000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>53227.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
+              "count     60.000000      60.0  ...    60.000000      60.000000\n",
+              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
+              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
+              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
+              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
+              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
+              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
+              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "a128e029-1ad2-43e2-f720-98f9f3cd5670",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>789</th>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1039</th>\n",
+              "      <td>1477</td>\n",
+              "      <td>0</td>\n",
+              "      <td>286</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1970</td>\n",
+              "      <td>RM</td>\n",
+              "      <td>80000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>868</th>\n",
+              "      <td>14762</td>\n",
+              "      <td>0</td>\n",
+              "      <td>672</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1948</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>169000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>782</th>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>10395</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>148000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "789     12205         0         444            8       1966       RL     187500\n",
+              "1039     1477         0         286            4       1970       RM      80000\n",
+              "868     14762         0         672            6       1948       RL     169000\n",
+              "782     16285         0         605            5       2001       RL     187100\n",
+              "611     10395         0         564            6       1978       RL     148000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "10db24ec-8525-476b-d43b-0ea991658a50",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>789</th>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1039</th>\n",
+              "      <td>1477</td>\n",
+              "      <td>0</td>\n",
+              "      <td>286</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1970</td>\n",
+              "      <td>RM</td>\n",
+              "      <td>80000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>868</th>\n",
+              "      <td>14762</td>\n",
+              "      <td>0</td>\n",
+              "      <td>672</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1948</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>169000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>782</th>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>611</th>\n",
+              "      <td>10395</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1978</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>148000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "789     12205         0         444            8       1966       RL     187500\n",
+              "1039     1477         0         286            4       1970       RM      80000\n",
+              "868     14762         0         672            6       1948       RL     169000\n",
+              "782     16285         0         605            5       2001       RL     187100\n",
+              "611     10395         0         564            6       1978       RL     148000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "77cc56c0-93f3-4e2b-f18f-a891e73ccd0d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[12205],\n",
+              "       [ 1477],\n",
+              "       [14762],\n",
+              "       [16285],\n",
+              "       [10395]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "24fde24a-67e1-4cc1-e510-cf6b82248f43",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[187500],\n",
+              "       [ 80000],\n",
+              "       [169000],\n",
+              "       [187100],\n",
+              "       [148000]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yzJ08rJ4J3CS",
+        "colab_type": "text"
+      },
+      "source": [
+        " Criando os subconjuntos de treinamento e testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tVz1mTUOJ4Tb",
+        "colab_type": "code",
+        "outputId": "e6abb0df-30fc-4bf8-9248-002ebcaa7ec9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
+        "\n",
+        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
+        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
+        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Tamanho do Dataset. 60\n",
+            "Tamanho do Conjunto de Treinamento. 48\n",
+            "Tamanho do Conjunto de Testes. 12\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#X_train = feature_scaling(X_train)\n",
+        "#X_test = feature_scaling(X_test)\n",
+        "\n",
+        "#X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "hDpjk2hPqBar"
+      },
+      "source": [
+        "### Etapa de Treinamento e Validação do Modelo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "5ZQOFczoqBat"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "84f2ff8a-ab60-4eda-a181-7505c2771d27",
+        "id": "TARpu6xZqBat",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SAyttRhcqsz_",
+        "colab_type": "text"
+      },
+      "source": [
+        "Transformando as features na quarta ordem polinomial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OZyCBmL2qs-X",
+        "colab_type": "code",
+        "outputId": "a171e5f2-8798-4ae4-ae51-f25c6094ac5a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "from sklearn.preprocessing import PolynomialFeatures\n",
+        "\n",
+        "poly_reg = PolynomialFeatures(degree = 4)\n",
+        "X_poly = poly_reg.fit_transform(X)\n",
+        "poly_reg.fit(X_poly, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PolynomialFeatures(degree=4, include_bias=True, interaction_only=False,\n",
+              "                   order='C')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "92IzIbgrrDdG",
+        "colab_type": "text"
+      },
+      "source": [
+        "Treinando o modelo de regressão polynomial com o conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AiAG4XSPrDy9",
+        "colab_type": "code",
+        "outputId": "2c6ea955-7780-45e6-a656-ccc9d73a5040",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "lin_reg_poly = LinearRegression()\n",
+        "lin_reg_poly.fit(X_poly, y)\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "5Xy5SEAuqBaz"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "3849351d-be10-4909-dab5-d433d530c716",
+        "id": "ySpZ9RyWqBa0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y_pred = lin_reg_poly.predict(X_poly)\n",
+        "\n",
+        "y_pred[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[210977.15912637],\n",
+              "       [101264.45016613],\n",
+              "       [231210.99529702],\n",
+              "       [242927.64582841],\n",
+              "       [195940.94028164]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wUrWgrvU0uTq",
+        "colab_type": "text"
+      },
+      "source": [
+        "Avaliando o modelo de regressão linear com a métrica r2:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lETAHBqw0uC5",
+        "colab_type": "code",
+        "outputId": "5e6b148c-5b36-47cd-a50c-0169ef1f2082",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.14645648066355865"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "f8hyc24cqBa7"
+      },
+      "source": [
+        "Avaliando o modelo de regressão polynomial com a métrica r2:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "056cb5e0-7c27-4758-f3da-32562a9d737a",
+        "id": "HDXwQ0ddqBa9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "lin_reg_poly.score(X_poly, y)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.2824360477057858"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "gaRZCCjKqBbC"
+      },
+      "source": [
+        "Visualizando os resultados da regressão linear:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "20b55d18-dc2d-46a7-ca33-d5336a6f027f",
+        "id": "GdApU1gYqBbD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_linear(X, y, regressor, 'Regressão Linear')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iM4T1CeU18eieezZ/Auztt0NSqakpTpmcKykFHi0kl+RyFjAfuDB+XouxnMVm\nqUOA52Po+5LmSrpdUmrQ8v5AYpollsRYtngfYK2ZbU2LNzlX3L4u7p9ervGS6iTVrVy5si23VLna\nU1UuoWa0a64JSWXZssbY3XeHpDJkSNGK5VzpKfCQTK2+RGlmnxCalia15wKSdgIeAi4ys/WSbgKu\nIbyUeQ1wHXB2e87dUWY2GZgMoUO/GGUoOYMGZe7ky1ZVLvATJ7l68UU47LCmsRNOgOnTO60IzpWf\nAg7JVNBRkSV1ISSWaWb2MICZvW9mDWa2DbiF0OwF4SGBgYnDB8RYtvgqoKek7dPiTc4Vt+8a93et\naW0mynRFbkb76KPwj670xGLmicW5YipYcol9HLcBr5vZrxLxPRK7fR14NS4/Cpwen/TaCxgKvAC8\nCAyNT4Z1JXT6P2rhGeqngNPi8eOARxLnGheXTwP+bD4rWm5SVeX0Sd9XrcrcsV/E+Wmk8HZ9kpl3\n1jtXCnJOLpJ2ik1cuToS+C5wbNpjxz+XNE/SXOAY4AcAZjYfuJ/Qp/MkcEGs4WwFvg9MJzwUcH/c\nF+BHwMWS6gl9KrfF+G1Anxi/GPj08WWXgzFjmn9rQ2ONJNnHkmlKRoDevaFv35ABpLCcp76Y1CmT\nNmzwpOJcKcllVOTPAncBvQkvUa4ExsVHkiuGv0SZJtu0xBCazdKbwpK6dMn8EkmXLmGClHa28Z54\nIjz5ZNPY8883bxJzznWejkxz/DvgYjMbbGaDgEuIneCugvXunTkuZU4sNTWNT5zsskvmtxO3bGlX\nX8y0aeHUycRy5ZUhf3lica405TLkfg8zSw0Fg5k9LalHAcvkSlm22sy2beED2ZvKoE19MfPmwec+\n1zS2227w/vs5n8I5VyS5JJeFkv4DuDuunwEsLFyRXElYvbpt+ycfVc72OHP6flls3Zp5XnrvU3Gu\nfOTSLHY20A94mPBYcV+K9F6K60TZkkCfPq2/1TtxInTt2vzYLl1afftXap5YGho8sThXbnJJLh8C\nF5rZoXHgyh/EmKtk2YaG+PWvmz+qvOOOTfcbMwZuv73pPn36tNiZn+kJsDfeCEmlpVY251xpyuV/\n22eA5HOpOwNPS9pH0gGFKZYrutaGhvj448Z9M70DM2YMfPBB44snH3yQMbEMHdo8qaQ66/ffP/+3\n5ZzrHLk8ijzHzIanxZYCvwbONbOhBSxfp/FHkdsgD/NA3HknnJVhhDpv/nKuvGR7FDmXDv2PJB1s\nZq/EEx0CvGtmP5e0W74L6spAB97KX74c9tijedyTinOVJZdmsYuAhyQ9I+lZwlv0FwCY2Q8LWTjX\nTq2NUtzRUYzbOQ+E1Dyx+GIjgkMAABZfSURBVHAtzlWmVpOLmb1ImBzsfOA8YJiZzS50wVw7ZZrs\n6+yzw/Ar220Xfp59dtsmA0tPRied1KZ5IHy4FueqT6vJRVJ3whheF8YhX4ZIOqXgJXPtk2mU4s2b\nQ6e7Wfi5eXPT7S2NYpwpWU2ZAuPGtToPRKak8uCD4TSZhi5zzlWOXPpc7gBmA1+I60uBB4DHClUo\n1wHtHY0423HZhtR//PGsnfeZ5vzs0SOM2u+cqw659LnsY2Y/B7YAmNlGwgCWrhS1d/7rbMe1ofP+\n5pszJxYzTyzOVZtckstmSTsSZo5E0j7ApoKWyrVftsm+WtLSvNk5dN6vWxeSynnnNd2lQ531JTR1\nsnOu7XJJLlcQ5lcZKGkaMBO4tKClcu2X/vJjnz7Nx1Pp0iXEc5k3O9ub+jEZSdCzZ9PN27Z1sLM+\nUz9Paw8dOOdKSosvUcbZJAcAG4EjCM1hs8zsg84pXuep6Jcop00LfSfvvBNqHBMntm1OlQzH64zm\nx8+aBYcfnofy5uElTedc52jXfC5xauDHzWyVmf3RzB7LNbFIGijpKUmvSZov6cIY7y1phqQF8Wev\nGJekGyTVS5or6dDEucbF/RdIGpeIj4izWtbHY9XSNarWmDHhS3nbtvAz18SSapr67nfD+t13o8WL\nmiWWffcNFYy8JBYo6tTJzrn8yKVZ7CVJn2/HubcCl5jZMEKt5wJJwwhTDs+Mw8bMpHEK4hOBofEz\nHrgJQqIgNM0dDhwGXJFIFjcB5yaOGxXj2a5RmQrRP5HWNJUpqUBIKgsWdPxyTbTzJU3nXOnIJbkc\nDsyS9FasUcyTNLe1g8xsmZm9FJc3AK8D/YHRwJS42xTg1Lg8GrjLgllAT0l7ACOBGWa22szWADOA\nUXHbLmY2K9aw7ko7V6ZrVJ589k8kk9S4cbBxI1MZg2jedFrQN+tb6edxzpW+XJLLSGBv4FjgH4FT\n4s+cSRoCHAI8D+xuZsvipuXA7nG5P/Bu4rAlMdZSfEmGOC1cI71c4yXVSapbuXJlW26pdGR7D6Wt\n0wmnJaltDdsQxneZ2mQ303bY1AI/ydXaiMzOuZKXNblI2kHSRcC/E5qblprZ4tQn1wtI2okwydhF\nZrY+uS3WOAo6CEhL1zCzyWZWa2a1/fr1K2QxCidbP8TixW1LAIkkJYwatjXZvJK+GILevTvnSa72\n9hM550pCSzWXKUAtMI/QH3JdW08uqQshsUwzs4dj+P3YpEX8uSLGlwIDE4cPiLGW4gMyxFu6RuXJ\n1g8htS0BvPMOwpo1gZ3F7RiiL6sam6ryUVNyzlW0lpLLMDM7w8x+B5wGfLktJ45Pbt0GvG5mv0ps\nehRIPfE1DngkER8bnxo7AlgXm7amAydI6hU78k8Apsdt6yUdEa81Nu1cma5ReTL1T0jNO0Q2bgz9\nKBkSjASybc3ihri9ZnzTpqnVqzOXw5/kcs4ltJRctqQWzGxrO859JPBd4FhJc+LnJOBa4KuSFgDH\nx3WAx4GFQD1wC2EUZsxsNXAN8GL8XB1jxH1ujce8BTwR49muUXky9U9k62lvaGhSgzn44CzDtcQ6\nDN27h0Eqk01T2WpKvXvn535S/A1958qbmWX8AA3A+vjZQHi0OLW8Pttx5foZMWKEVYzBg1MPc2X8\nLNjzqIybbOrUcKwUfk6d2vzcU6eade3a/OAuXTLv3x5Tp5p17970/N275+/8zrm8Aeosw3dqq9Mc\nV4uKekM/9eRXet8IZH2suE369g1D96fL1xv0/oa+c2WjXW/ouzKVaiqrqfk0lKmzfubMdr6rUuh+\nF39D37my58mlkvXsmTGpQEgqxx7bzvMW+g16f0PfubLnyaWSpDrBJXTGGLSq+TBwNnVax9+sL/Qb\n9P6GvnNlz5NLpZg2Dc46i/9ZvG/mmgrCBg/Jz8uIhX6D3t/Qd67seYd+VO4d+g19dmP71c3fFbXk\npKFSeKzYOefyxDv0K5hEs8Syia5NEws09lm05R0Sf9/EOdcOnlzKmNT8Jchn+RKG6Nr4DmyQ6rNo\nyyjKhZoR0hOWcxXPk0sZGjmyeVK5oNutGOJLPNf8gGSfRVtGUc7XiMtJPoWxc1XB+1yicuhzmTMH\nDjmkedyM8OV89tmweXPjhq5d4fbbm3aEb7dd5pdbMvXHtGXfXPkLks5VFO9zKWMbN4bv8/TE0mTC\nrjFjQiJJPmGVnligbe+QFOJ9E39B0rmq4MmlxEnQo0fTWNZZIHOZA6Ut75AU4n0Tf0HSuargyaVE\nZeqs37gxD1MLt+UdkkK8b+IvSDpXFbzPJSqVPpdx4+Cuu5rG5s2Dgw4qTnkKYtq08FDAO++EGsvE\nif6CpHNlyvtcStzvfx8qB8nE8tvfhppKXhJLKT3+61MYO1fxPLkU2ZIlIal84xuNsS9/OSSV88/P\n4QS5JA1//Nc518m8WSzq7GaxhgbYfvvm8Tb958g0b0v37s37RfzxX+dcgXR6s5ik2yWtkPRqInal\npKVp0x6ntl0uqV7Sm5JGJuKjYqxe0mWJ+F6Sno/x+yR1jfFucb0+bh9SqHtsr4EDmyeWbdva0Vmf\n7SXHceOa1kpaevy3lJrLnHMVo5DNYncCozLEJ5nZ8Ph5HEDSMOB04MB4zI2SaiTVAL8FTgSGAf8c\n9wX4WTzXvsAa4JwYPwdYE+OT4n6dp4Uv63/7t9AEtmRJ4+4rV4akkmku+1ZlSxoNDU2bvVqa9z69\nuezss8NMk55snHMdULDkYmbPAFmmLGxmNHCvmW0ys7eBeuCw+Kk3s4Vmthm4FxgtScCxwIPx+CnA\nqYlzTYnLDwLHxf0LL0vfxsMXPYME//mfjbvOnRt26du3A9dr6d2Q5DAt2R7/Te2XtHlzmMLY+2ac\ncx1QjA7970uaG5vNesVYf+DdxD5LYixbvA+w1sy2psWbnCtuXxf3b0bSeEl1kupWrlzZ8TtLa6Za\nwL5o40f806+P+jQ2ZUr43v7sZzt+uYxJIylVs8n2vkq26YqTOjqWmHOuKnV2crkJ2AcYDiwDruvk\n6zdhZpPNrNbMavv169fxE8Yv843siDD2Y8Gnm8aODUll7NiOX+ZTqaRRU5N5e7Jmk+nx31zfiveh\nWTrO+7ZclenU5GJm75tZg5ltA24hNHsBLAUGJnYdEGPZ4quAnpK2T4s3OVfcvmvcv/AGDeISfkkP\nGmsvO7IRGzyEKVNaOK4jxowJ1aG2vPWe+qJbvDi3zh4z/0LsCH8U3FUjMyvYBxgCvJpY3yOx/ANC\nPwuEjvxXgG7AXsBCoAbYPi7vBXSN+xwYj3kAOD0u3wycH5cvAG6Oy6cD9+dS1hEjRlhH3HFHasSv\nxs8Wasy6dzebOrVD587J1KlmgwebSeFntmtOnRrKlCyoFH726WPWpUvzG0l9OuteKs3gwZl/n4MH\nF7tkznUYUGeZvv8zBfPxAe4hNH1tIfSJnAPcDcwD5gKPpiWbCcBbwJvAiYn4ScDf47YJifjewAuE\nzv8HgG4xvkNcr4/b986lvO1NLosXN/2+2Kvfels/cFjrX/LF0toXXSpJZUswqX1L7b5KWSp5p3+k\nYpfMuQ7Lllz8JcqovS9RTpoEF18clt9+O7QedbrkWF29e4fY6tWZx+3KdY6WbPulZHpZ02XmL7G6\nCuZjixXIv/4rbNjQ2C3R6dLb81etavlR4lyHvG+ts9+fIsudjwTtqpAnlw7afnvYaaciFiDTW/pJ\n6Ukg1y+61h5zBn+KLFeFmLrAuRKXYXQrV1Zy+YJP7pP6QmttyPvkfpmadMAn+GqLMWM8mbiq4jWX\ncpfLF3z6PrkOeZ/ab+pUb9ZxzrWJJ5dy11rzVT6SgDfrOOfayJ8Wi0plJsp2yfS02KrEe6M1NaFj\n/8Ybi1M+51zF8qfFKlmymeuDD+Bb32q6vaEBbropx9nHnHOu4zy5lJNcx6eaPLlt8Xxf3zlX9fxp\nsXKRPutk6h0WaN730dCQ+RzZ4vm+vnOu6nnNpVxkm3Uy04uM2UZJhvbXNtpyfedc1fPkUi5amqo4\nXapGkcn48aHvpa3NW225vnOu6nlyKRe5DtsC4amw887LvP/GjXDzzW0f/r0t13fOVT1PLuWireNT\n3Xhj9rla0h8/z6V5y8fHcs61gSeXctGeFxnbUqtorXnLX6R0zrWBv0QZlfVLlNmkP+EFITFk+m/u\nw78759rBX6KsRplqG9/7njdvOecKzpNLpUsfpPLGG715yzlXcAVLLpJul7RC0quJWG9JMyQtiD97\nxbgk3SCpXtJcSYcmjhkX918gaVwiPkLSvHjMDVLovc52jarS2pv0uY6K7Jxz7VTImsudwKi02GXA\nTDMbCsyM6wAnAkPjZzxwE4REAVwBHA4cBlyRSBY3AecmjhvVyjWqQ/rMlLk+auycc3lUsORiZs8A\nq9PCo4EpcXkKcGoifpcFs4CekvYARgIzzGy1ma0BZgCj4rZdzGyWhScS7ko7V6ZrVAd/k945VwI6\nu89ldzNbFpeXA7vH5f7Au4n9lsRYS/ElGeItXaMZSeMl1UmqW7lyZTtupwhaa/LyN+mdcyWgaB36\nscZR0OegW7uGmU02s1ozq+3Xr18hi5IfuTR5+Zv0zrkS0NnJ5f3YpEX8uSLGlwIDE/sNiLGW4gMy\nxFu6RvnLpcnL36R3zpWAzk4ujwKpJ77GAY8k4mPjU2NHAOti09Z04ARJvWJH/gnA9LhtvaQj4lNi\nY9POleka5S+XJi9/k945VwIKNp+LpHuAo4G+kpYQnvq6Frhf0jnAYiA1ZeLjwElAPbAROAvAzFZL\nugZ4Me53tZmlHhI4n/BE2o7AE/FDC9cof4MGhaawTPGkMWM8mTjnisqHf4nKYviXTMO5dO/uNRPn\nXNH48C+VoBSavHyqY+dcDjy5dEQxvmiL+Xa9v6DpnMuRJ5f2qsYvWn9B0zmXI08u7VWNX7T+gqZz\nLkeeXNqrGr9o/QVN51yOPLm0VzV+0foLms65HHlyaa9q/KIthafVnHNloWAvUVa81BfqhAmhKWzQ\noJBYKv2L1l/QdM7lwJNLR/gXrXPOZeTNYs455/LOk4tzzrm88+TinHMu7zy5OOecyztPLs455/LO\nh9yPJK0kzP+Sri/wQScXp1j8XiuT32tlKpV7HWxmzeaJ9+TSCkl1meYqqER+r5XJ77Uylfq9erOY\nc865vPPk4pxzLu88ubRucrEL0In8XiuT32tlKul79T4X55xzeec1F+ecc3nnycU551zeeXJpgaRR\nkt6UVC/psmKXJ1eSbpe0QtKriVhvSTMkLYg/e8W4JN0Q73GupEMTx4yL+y+QNC4RHyFpXjzmBknq\n3Dv8tBwDJT0l6TVJ8yVdGOOVeK87SHpB0ivxXq+K8b0kPR/Ld5+krjHeLa7Xx+1DEue6PMbflDQy\nES+pv3dJNZJelvRYXK/Ie5W0KP6NzZFUF2Pl/zdsZv7J8AFqgLeAvYGuwCvAsGKXK8eyHwUcCrya\niP0cuCwuXwb8LC6fBDwBCDgCeD7GewML489ecblX3PZC3Ffx2BOLdJ97AIfG5Z2BvwPDKvReBewU\nl7sAz8dy3Q+cHuM3A+fF5fOBm+Py6cB9cXlY/FvuBuwV/8ZrSvHvHbgY+C/gsbhekfcKLAL6psXK\n/m/Yay7ZHQbUm9lCM9sM3AuMLnKZcmJmzwCr08KjgSlxeQpwaiJ+lwWzgJ6S9gBGAjPMbLWZrQFm\nAKPitl3MbJaFv9y7EufqVGa2zMxeissbgNeB/lTmvZqZfRhXu8SPAccCD8Z4+r2mfgcPAsfFf7GO\nBu41s01m9jZQT/hbL6m/d0kDgJOBW+O6qNB7zaLs/4Y9uWTXH3g3sb4kxsrV7ma2LC4vB3aPy9nu\ns6X4kgzxoopNIYcQ/kVfkfcam4nmACsIXx5vAWvNbGuG8n16T3H7OqAPbf8dFMv1wKXAtrjeh8q9\nVwP+JGm2pPExVvZ/wz4TZRUyM5NUMc+gS9oJeAi4yMzWJ5uUK+lezawBGC6pJ/B74IAiF6kgJJ0C\nrDCz2ZKOLnZ5OsGXzGyppN2AGZLeSG4s179hr7lktxQYmFgfEGPl6v1YRSb+XBHj2e6zpfiADPGi\nkNSFkFimmdnDMVyR95piZmuBp4AvEJpFUv9ITJbv03uK23cFVtH230ExHAl8TdIiQpPVscCvqcx7\nxcyWxp8rCP9oOIxK+BvurE6rcvsQanULCR2BqU6/A4tdrjaUfwhNO/R/QdMOwp/H5ZNp2kH4Qoz3\nBt4mdA72isu947b0DsKTinSPIrQhX58Wr8R77Qf0jMs7As8CpwAP0LST+/y4fAFNO7nvj8sH0rST\neyGhg7sk/96Bo2ns0K+4ewV6ADsnlv8GjKqEv+Gi/uGU+ofwZMbfCW3bE4pdnjaU+x5gGbCF0MZ6\nDqENeiawAPifxB+egN/Ge5wH1CbOczahE7QeOCsRrwVejcf8hjjSQxHu80uE9uq5wJz4OalC7/Vz\nwMvxXl8Ffhzje8cvj/r45dstxneI6/Vx+96Jc02I9/MmiSeHSvHvnabJpeLuNd7TK/EzP1WWSvgb\n9uFfnHPO5Z33uTjnnMs7Ty7OOefyzpOLc865vPPk4pxzLu88uTjnnMs7Ty6uKknqE0ehnSNpuaSl\nifWunVyWJfGt+3yc66+Shhdif0mHShrV/tK5auLDv7iqZGargOEAkq4EPjSzXxa1UKXvUOAg4Mli\nF8SVPq+5OJdG0h/iIILzJf1LjG0vaa2kX8X4dEmHS/qLpIWSTor77SPp2TgPyWxJh8f48ZJmSno4\nziNyV9plL4rHzJW0Xzymr6RHY+xvkg7KUNbukh6Q9LqkhwgvFKa2nRHn8XhV0k/bcP87SpoSj31J\n0lGSdgR+DIyJtbvTJO0k6U6FeWZelvSPbf1du8rlycW55saZ2Qjg88DFihM1EcasesLMDgQ2A1cC\nxwHfBK6O+ywDvmpmhwBjgBsS5z0U+D5hnpHPSDoise39eMythHlMAK4hzNfxuXitOzOU9fvAGjP7\nDPATwsjQqSHrfwIcE2NHxgEhc/FvwCYz+yzwXeBuoCHe4zQzG25mDxKSzZNmdhhh/K/rJO2Q7aSu\nunhyca65H0h6BfhfwkB/+8T4x2Y2Iy7PA562MMT7PMJYbhDGsbpNYRbQewmJJGWWmb1nYXTjOYlj\nAFKDbs5OxL9E+GLHzP4E7CmpR1pZjwKmxn1eJgwhAnA48Gcz+8DMthAm3Toqx/v/UuKc84H3gH0z\n7HcCMCFOA/AUodY0KMdruArnfS7OJUg6nvAlfISZfSzprzQ2NW1O7LoN2JRYTv2/dAlhXo0zCBN6\nfZg4ZlNiuYGm//9tyhIvZQJONbO3il0QV3q85uJcU7sCq2NiOZDQNNbW45dZGLRvHOELuL2eJTSt\npZLeUjP7KG2fZ4DvxH0OJowEDGHStGPiU3HbE0YL/ks7rvsZwnTS9cAGwnTSKdOBf02tSDok5ztz\nFc+Ti3NN/RHoLuk1Qp/F8208/jfAv8Rmtb1oWltpqx8DX5A0l9DfcVaW6/WR9DrwH4SRkzGzJXH9\naUIT3Cwz+2OW60yPj0MvkXQP8J/AjpLmAdOAsRamA/4zcHDsvD8NuAroETv+5xP6hZwD8FGRnXPO\n5Z/XXJxzzuWdJxfnnHN558nFOedc3nlycc45l3eeXJxzzuWdJxfnnHN558nFOedc3v1/6WaF+1fN\n62UAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "LlX6EwYwqBbG"
+      },
+      "source": [
+        "Visualizando os resultados da regressão linear polinomial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "a8a22b70-a788-4185-f249-733db49e81e9",
+        "id": "5GKwud8fqBbG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        }
+      },
+      "source": [
+        "plot_results_poly(X, y, lin_reg_poly, poly_reg, 'Regressão Polynomial')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ptp13hv/9zxJIXnCBKZZNm2w7nT32gKOPhrlzLZXLnDnmHe6Kxam1JForK1uw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\nTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From a0a2adb38ebde3ecd88fc0d04d8153f309c6b066 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:06:48 -0300
Subject: [PATCH 08/14] Delete Regressao_Logistica.ipynb

---
 notebooks/2_semana/Regressao_Logistica.ipynb | 1287 ------------------
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deleted file mode 100644
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@@ -1,1287 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Regressao_Logistica.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "0KkcOxCxjptF",
-        "8CLZyvMlh_Qj",
-        "gIqtlsYGmOYq"
-      ],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Logistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "#Modelos de Classificação : Regressão Logística"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0KkcOxCxjptF",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs  e funções"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# Importando libs de visualização de dados\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns; sns.set()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função que calcula os reais positivos\n",
-        "def rp(tp, fn):\n",
-        "    return tp + fn\n",
-        "\n",
-        "# Função que calcula os reais negativos     \n",
-        "def rn(fp, tn):\n",
-        "    return fp + tn\n",
-        "\n",
-        "# Função que calcula as predicoes positivas  \n",
-        "def pp(tp, fp):\n",
-        "    return tp + fp\n",
-        "\n",
-        "# Função que calcula as predicoes negativas   \n",
-        "def pn(fn, tn):\n",
-        "    return fn + tn\n",
-        "\n",
-        "# Função que calcula acurácia do modelo\n",
-        "def accuracy (tp, fp, fn, tn):\n",
-        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
-        "     return (accuracy)\n",
-        "    \n",
-        "# Função que calcula a precisão \n",
-        "def precision (tp, fp):\n",
-        "    precision =  (tp / (tp + fp)) #predições positivas\n",
-        "    return precision\n",
-        "\n",
-        "# Função que calcula o recall\n",
-        "def recall(tp, fn):\n",
-        "    recall =  (tp / (tp + fn)) # reais positivos\n",
-        "    return recall\n",
-        "\n",
-        "## Função que calcula o f-measure (media harmonica entre precision e recall)\n",
-        "def f_measure(tp, fp, fn):\n",
-        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
-        "    return f_measure\n",
-        "  \n",
-        "# Função que calcula o Informedness \n",
-        "def informedness(tp, fp, fn, tn):\n",
-        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
-        "    return inform\n",
-        "\n",
-        "# Função que calcula o Markedness\n",
-        "def markdness(tp, fp, fn, tn):    \n",
-        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
-        "    return mark\n",
-        "\n",
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de classificação\n",
-        "def plot_results_class(X, y, classifier, title):\n",
-        "    X_set, y_set = X, y\n",
-        "    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
-        "                         np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
-        "    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
-        "                 alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
-        "    plt.xlim(X1.min(), X1.max())\n",
-        "    plt.ylim(X2.min(), X2.max())\n",
-        "    for i, j in enumerate(np.unique(y_set)):\n",
-        "        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
-        "                    c = ListedColormap(('red', 'green'))(i), label = j)\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Idade')\n",
-        "    plt.ylabel('Tarifa')\n",
-        "    plt.legend()\n",
-        "    plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8CLZyvMlh_Qj",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos dados"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando os dados dos passageiros do titanic e selecionando uma amostragem dos dados para uma melhor visualização. O objetivo dessa tarefa de classificação é prever os passageiros sobreviventes com base nas suas informações pessoais.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/titanic/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
-        "df = df.sample(n=100, random_state=0, replace = True)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "6f217547-c6ba-4f14-ae3a-da80b4096355",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 238
-        }
-      },
-      "source": [
-        "# Exporando o dataset\n",
-        "df.info()"
-      ],
-      "execution_count": 12,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 100 entries, 44 to 20\n",
-            "Data columns (total 8 columns):\n",
-            "Unnamed: 0     100 non-null int64\n",
-            "LotArea        100 non-null int64\n",
-            "PoolArea       100 non-null int64\n",
-            "GarageArea     100 non-null int64\n",
-            "OverallCond    100 non-null int64\n",
-            "YearBuilt      100 non-null int64\n",
-            "MSZoning       100 non-null object\n",
-            "SalePrice      100 non-null int64\n",
-            "dtypes: int64(7), object(1)\n",
-            "memory usage: 7.0+ KB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "HQOuXFFIi8OZ",
-        "colab_type": "code",
-        "outputId": "d621591a-6226-4107-8eca-39e26ee223c7",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "# Visualizando o sumário das colunas numéricas do dataset\n",
-        "df.describe()"
-      ],
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Unnamed: 0</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.0</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>775.810000</td>\n",
-              "      <td>10898.090000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>522.930000</td>\n",
-              "      <td>5.890000</td>\n",
-              "      <td>1970.320000</td>\n",
-              "      <td>199800.560000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>394.243674</td>\n",
-              "      <td>6259.640208</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>233.049232</td>\n",
-              "      <td>1.301863</td>\n",
-              "      <td>31.138297</td>\n",
-              "      <td>76041.565893</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>26.000000</td>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>546.750000</td>\n",
-              "      <td>8381.250000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.000000</td>\n",
-              "      <td>148375.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>802.000000</td>\n",
-              "      <td>10760.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>551.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.000000</td>\n",
-              "      <td>195000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>1039.000000</td>\n",
-              "      <td>12068.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>616.500000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>1442.000000</td>\n",
-              "      <td>46589.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "        Unnamed: 0       LotArea  ...    YearBuilt      SalePrice\n",
-              "count   100.000000    100.000000  ...   100.000000     100.000000\n",
-              "mean    775.810000  10898.090000  ...  1970.320000  199800.560000\n",
-              "std     394.243674   6259.640208  ...    31.138297   76041.565893\n",
-              "min      26.000000   1477.000000  ...  1875.000000   80000.000000\n",
-              "25%     546.750000   8381.250000  ...  1950.000000  148375.000000\n",
-              "50%     802.000000  10760.500000  ...  1976.000000  195000.000000\n",
-              "75%    1039.000000  12068.750000  ...  2002.000000  235032.000000\n",
-              "max    1442.000000  46589.000000  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 7 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "fb164251-d4c9-4c14-e780-bde3b35799f8",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>PassengerId</th>\n",
-              "      <th>Survived</th>\n",
-              "      <th>Pclass</th>\n",
-              "      <th>Name</th>\n",
-              "      <th>Sex</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>SibSp</th>\n",
-              "      <th>Parch</th>\n",
-              "      <th>Ticket</th>\n",
-              "      <th>Fare</th>\n",
-              "      <th>Cabin</th>\n",
-              "      <th>Embarked</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>495</th>\n",
-              "      <td>496</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Yousseff, Mr. Gerious</td>\n",
-              "      <td>male</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2627</td>\n",
-              "      <td>14.4583</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>648</th>\n",
-              "      <td>649</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Willey, Mr. Edward</td>\n",
-              "      <td>male</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>S.O./P.P. 751</td>\n",
-              "      <td>7.5500</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>278</th>\n",
-              "      <td>279</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Rice, Master. Eric</td>\n",
-              "      <td>male</td>\n",
-              "      <td>7.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1</td>\n",
-              "      <td>382652</td>\n",
-              "      <td>29.1250</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>Q</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>31</th>\n",
-              "      <td>32</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
-              "      <td>female</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>PC 17569</td>\n",
-              "      <td>146.5208</td>\n",
-              "      <td>B78</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>255</th>\n",
-              "      <td>256</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Touma, Mrs. Darwis (Hanne Youssef Razi)</td>\n",
-              "      <td>female</td>\n",
-              "      <td>29.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2650</td>\n",
-              "      <td>15.2458</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     PassengerId  Survived  Pclass  ...      Fare Cabin  Embarked\n",
-              "495          496         0       3  ...   14.4583   NaN         C\n",
-              "648          649         0       3  ...    7.5500   NaN         S\n",
-              "278          279         0       3  ...   29.1250   NaN         Q\n",
-              "31            32         1       1  ...  146.5208   B78         C\n",
-              "255          256         1       3  ...   15.2458   NaN         C\n",
-              "\n",
-              "[5 rows x 12 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "705d845a-fa8d-4291-9e72-41ce575bf8ee",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>PassengerId</th>\n",
-              "      <th>Survived</th>\n",
-              "      <th>Pclass</th>\n",
-              "      <th>Name</th>\n",
-              "      <th>Sex</th>\n",
-              "      <th>Age</th>\n",
-              "      <th>SibSp</th>\n",
-              "      <th>Parch</th>\n",
-              "      <th>Ticket</th>\n",
-              "      <th>Fare</th>\n",
-              "      <th>Cabin</th>\n",
-              "      <th>Embarked</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>495</th>\n",
-              "      <td>496</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Yousseff, Mr. Gerious</td>\n",
-              "      <td>male</td>\n",
-              "      <td>26.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2627</td>\n",
-              "      <td>14.4583</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>648</th>\n",
-              "      <td>649</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Willey, Mr. Edward</td>\n",
-              "      <td>male</td>\n",
-              "      <td>26.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>S.O./P.P. 751</td>\n",
-              "      <td>7.5500</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>S</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>278</th>\n",
-              "      <td>279</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Rice, Master. Eric</td>\n",
-              "      <td>male</td>\n",
-              "      <td>7.0</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1</td>\n",
-              "      <td>382652</td>\n",
-              "      <td>29.1250</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>Q</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>31</th>\n",
-              "      <td>32</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
-              "      <td>female</td>\n",
-              "      <td>26.0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>PC 17569</td>\n",
-              "      <td>146.5208</td>\n",
-              "      <td>B78</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>255</th>\n",
-              "      <td>256</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>Touma, Mrs. Darwis (Hanne Youssef Razi)</td>\n",
-              "      <td>female</td>\n",
-              "      <td>29.0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>2650</td>\n",
-              "      <td>15.2458</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>C</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     PassengerId  Survived  Pclass  ...      Fare Cabin  Embarked\n",
-              "495          496         0       3  ...   14.4583   NaN         C\n",
-              "648          649         0       3  ...    7.5500   NaN         S\n",
-              "278          279         0       3  ...   29.1250   NaN         Q\n",
-              "31            32         1       1  ...  146.5208   B78         C\n",
-              "255          256         1       3  ...   15.2458   NaN         C\n",
-              "\n",
-              "[5 rows x 12 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes e visualizando as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "0187a2d8-9a56-42f8-f315-176ec59a1219",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X = df.iloc[:, [5, 9]].values\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[ 26.    ,  14.4583],\n",
-              "       [ 26.    ,   7.55  ],\n",
-              "       [  7.    ,  29.125 ],\n",
-              "       [ 26.    , 146.5208],\n",
-              "       [ 29.    ,  15.2458]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes e visualizando as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "42cd730b-3886-48a1-e117-54da7663f8db",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "y = df.iloc[:, 1].values\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0, 0, 0, 1, 1])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yzJ08rJ4J3CS",
-        "colab_type": "text"
-      },
-      "source": [
-        " Criando os subconjuntos de treinamento e testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tVz1mTUOJ4Tb",
-        "colab_type": "code",
-        "outputId": "c97d6fa7-10ea-4bec-c1d6-a67ad0a0b8c7",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 42)\n",
-        "\n",
-        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
-        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
-        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Tamanho do Dataset. 100\n",
-            "Tamanho do Conjunto de Treinamento. 80\n",
-            "Tamanho do Conjunto de Testes. 20\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features e exibindo as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "outputId": "1bbd577a-6f18-4d0b-c924-4f4d0baa371e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X_train = feature_scaling(X_train)\n",
-        "X_test = feature_scaling(X_test)\n",
-        "\n",
-        "X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[-0.2233842 ,  0.62589282],\n",
-              "       [ 0.50330481, -0.63776468],\n",
-              "       [ 0.35796701, -0.68999987],\n",
-              "       [-0.0780464 , -0.55753246],\n",
-              "       [-0.58672871, -0.2466326 ]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jGXuG35WJ_Tj",
-        "colab_type": "text"
-      },
-      "source": [
-        "Treinando o modelo de Árvore de Decisão com o Conjunto de Treinamento"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "gIqtlsYGmOYq"
-      },
-      "source": [
-        "### Etapa de Treinamento e Validação do Modelo"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "oF9z-OwymOYs"
-      },
-      "source": [
-        "Importando e treinando o modelo com o conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "8d5f3b06-3372-42a4-fe53-7affbad191ba",
-        "id": "G1uuhHzumOYt",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 136
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LogisticRegression\n",
-        "\n",
-        "classifier = LogisticRegression(random_state = 0)\n",
-        "classifier.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-            "  FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-              "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
-              "                   warm_start=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "sKGRDMbWmOY1"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "10be0e36-54f6-4530-8378-9c4b2f72ff8e",
-        "id": "FAvYq8NemOY3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "y_pred = classifier.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "2qVdkDyzmOY_"
-      },
-      "source": [
-        "Criando e exibindo os valores da matriz de confusão com o conjunto de testes "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "412d2fd2-d655-4a5c-c8c0-b906049447ec",
-        "id": "dzm-N1R7mOZD",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
-        "\n",
-        "confusion_matrix(y_test, y_pred)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[14,  1],\n",
-              "       [ 3,  2]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "Vl2JrA-cmOZI"
-      },
-      "source": [
-        "Visualizando a matriz de confusão"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "b66d506b-fa33-4666-a495-c5587d614bf7",
-        "id": "o8x_wA2CmOZJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 241
-        }
-      },
-      "source": [
-        "mat = confusion_matrix(y_test, y_pred)\n",
-        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
-        "plt.xlabel('true label')\n",
-        "plt.ylabel('predicted label');"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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92rB9lfpm93Y6VosW5XQAhF5FpVfz5r+o/v36qk2b1k7HAYIio6JUWV6loYP+TeWfVSqr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\nBADAYJQAABiMEgAAg1ECAGCw/wOL4LBa8GRKHAAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "y6TAt9MomOZL"
-      },
-      "source": [
-        "Visualizando a métrica de **acurácia** utilizando as funções criadas e a bibilioteca sklearn:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "680fcd92-32f2-4554-b847-0b75b5942e39",
-        "id": "rMMCqczomOZM",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "accuracy(tp, fp, fn, tn)\n",
-        "classifier.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "rAL1ztqJmOZP"
-      },
-      "source": [
-        "Exibindo a medida **f-measure**:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "60cff7ee-505a-4dc2-eb59-73785668ef2f",
-        "id": "dcg2oWJImOZQ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "f_measure(tp, fp, fn)\n",
-        "f1_score(y_test, y_pred)  "
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "6SG8SzeXmOZS"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "8e82d219-2e85-4201-a88e-b4cbecb69c00",
-        "id": "_xENnpTxmOZU",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 355
-        }
-      },
-      "source": [
-        "plot_results_class(X_train, y_train, classifier, 'Regressão Logística (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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tuYJDPYcXX3wRjRs3BgAsWrQIxcXFuHr1Kl5++eUaDY7IUdU9IHpSSYrTackV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\ncgAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "QwmncO8mmOZa"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "62772bbd-82e5-4f3a-fe3c-2f1f92430914",
-        "id": "tA-DeHAMmOZb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 355
-        }
-      },
-      "source": [
-        "plot_results_class(X_test, y_test, classifier, 'Regressão Logística (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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RmjRpAgBwcnJCcXExWrVqhcuXL1u1OCIikkeF1hyef/55XL58Ge3bt0fbtm0R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\nAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 6c94e69f38e91512ca2be5e5332c034d3a6ab25d Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:06:55 -0300
Subject: [PATCH 09/14] Delete Regressao_Multipla.ipynb

---
 notebooks/2_semana/Regressao_Multipla.ipynb | 1219 -------------------
 1 file changed, 1219 deletions(-)
 delete mode 100644 notebooks/2_semana/Regressao_Multipla.ipynb

diff --git a/notebooks/2_semana/Regressao_Multipla.ipynb b/notebooks/2_semana/Regressao_Multipla.ipynb
deleted file mode 100644
index 093cfa3..0000000
--- a/notebooks/2_semana/Regressao_Multipla.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Regressao_Multipla.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "TsV0SiuwpZ7i",
-        "QaGBc_KwpeVt",
-        "VUZAwJjMvh0r"
-      ],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Multipla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Modelos de Regressão: Regressão Linear Múltipla"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TsV0SiuwpZ7i",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs e funções:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regressão\n",
-        "def plot_results_linear(X, y, regressor, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
-        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()    \n",
-        "    \n",
-        "# Função que gera o gráfico dos resultados de arvores\n",
-        "def plot_results_reg(X, y, regressor, title):     \n",
-        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
-        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "     "
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QaGBc_KwpeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos **dados**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
-        "\n",
-        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "bcace5e2-035f-4e8b-ed04-d21036a25367",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "df.describe()"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.0</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>11633.933333</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>504.683333</td>\n",
-              "      <td>5.783333</td>\n",
-              "      <td>1972.116667</td>\n",
-              "      <td>197536.150000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>8099.879028</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>222.427554</td>\n",
-              "      <td>1.180228</td>\n",
-              "      <td>30.522618</td>\n",
-              "      <td>74910.507145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>8068.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.750000</td>\n",
-              "      <td>142250.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>10510.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>542.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.500000</td>\n",
-              "      <td>188750.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>12010.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>607.250000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>53227.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
-              "count     60.000000      60.0  ...    60.000000      60.000000\n",
-              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
-              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
-              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
-              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
-              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
-              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
-              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 6 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "b06300d2-add2-4c00-f192-52a2e732edf8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 237
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "11e16af0-7111-42a8-fa5a-b813693d64df",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 238
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "fc0ba041-626e-403f-f36e-a0b3fbfe3415",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[ 8450],\n",
-              "       [ 9600],\n",
-              "       [11250],\n",
-              "       [ 9550],\n",
-              "       [14260]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 239
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "d7c38a52-ee55-478a-9f50-af39542fe538",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[208500],\n",
-              "       [181500],\n",
-              "       [223500],\n",
-              "       [140000],\n",
-              "       [250000]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 240
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "VUZAwJjMvh0r"
-      },
-      "source": [
-        "###  Etapa de Classificação e Validação do Modelo"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZOIXAi6Wvpce",
-        "colab_type": "text"
-      },
-      "source": [
-        "Selecionando as principais features do dataset:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "et8CNkI1vpk2",
-        "colab_type": "code",
-        "outputId": "5b82c6a2-0346-4de7-f897-68ec246fd933",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].reindex()\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 241
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZjC2LJYh1SQ6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
-        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SeWh_rpnYkLd",
-        "colab_type": "code",
-        "outputId": "58ec4049-ab3a-4c72-be4c-921620731399",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "df['MSZoning'].unique()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 242
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eumBvAB8wbq-",
-        "colab_type": "text"
-      },
-      "source": [
-        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rjT1ONG7wb9x",
-        "colab_type": "code",
-        "outputId": "cd492757-3b55-42bc-feb6-43de14ff9341",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "      <th>MSZoning_FV</th>\n",
-              "      <th>MSZoning_RH</th>\n",
-              "      <th>MSZoning_RL</th>\n",
-              "      <th>MSZoning_RM</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>208500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>181500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>223500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>140000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>250000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
-              "0     8450         0         548  ...            0            1            0\n",
-              "1     9600         0         460  ...            0            1            0\n",
-              "2    11250         0         608  ...            0            1            0\n",
-              "3     9550         0         642  ...            0            1            0\n",
-              "4    14260         0         836  ...            0            1            0\n",
-              "\n",
-              "[5 rows x 10 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 243
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i9fs5x8MvpxV",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "b9uRFW2vvp6b",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
-        "y = df['SalePrice'].values.reshape(-1,1)\n",
-        "\n",
-        "# Dividindo os dados\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "outputId": "8fa737f1-578b-4644-de0f-b1ee66fcc7d0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 187
-        }
-      },
-      "source": [
-        "X_train = feature_scaling(X_train)\n",
-        "X_test = feature_scaling(X_test)\n",
-        "\n",
-        "X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[-0.21289571, -0.07099284, -0.86383727,  0.3722173 , -0.45546896,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.26524463, -0.07099284, -0.45626397,  1.26860866,  0.71860895,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.17784146, -0.07099284, -2.25716927,  1.26860866, -1.98829291,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.32447422, -0.07099284, -1.11975539,  1.26860866, -1.10773447,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.52903482, -0.07099284, -0.79748813,  0.3722173 , -1.53170705,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 245
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "SMaN0iHNvh0t"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "0ce99d9c-c744-4034-fec4-7cdc01565225",
-        "id": "VuzaRxyuvh0u",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "\n",
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 246
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "zT6PUXkWvh0y"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "1da72340-52a8-4022-a2e5-363eb7f76251",
-        "id": "JR-E180Fvh0y",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[162102.94940288],\n",
-              "       [241746.81922749],\n",
-              "       [119733.72469521],\n",
-              "       [147800.757112  ],\n",
-              "       [285853.71056469]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 247
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "PO6sGI3Gvh01"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "6f12ba33-16a7-4559-de51-fb0201398258",
-        "id": "fQPgnd8dvh02",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5043465841755175"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 248
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 34b07b31c82e5f3a1368dc4b3ed93848bb3caad3 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:07:01 -0300
Subject: [PATCH 10/14] =?UTF-8?q?Delete=20Regress=C3=A3o=5FPolynomial.ipyn?=
 =?UTF-8?q?b?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../Regress\303\243o_Polynomial.ipynb"        | 1087 -----------------
 1 file changed, 1087 deletions(-)
 delete mode 100644 "notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"

diff --git "a/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb" "b/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"
deleted file mode 100644
index 8e0d84d..0000000
--- "a/notebooks/2_semana/Regress\303\243o_Polynomial.ipynb"
+++ /dev/null
@@ -1,1087 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Regressão_Polynomial.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "TsV0SiuwpZ7i",
-        "QaGBc_KwpeVt",
-        "hDpjk2hPqBar"
-      ],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regress%C3%A3o_Polynomial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Modelos de Regressão: Regressão Polynomial"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TsV0SiuwpZ7i",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs e funções:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regressão\n",
-        "def plot_results_linear(X, y, regressor, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
-        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()    \n",
-        "    \n",
-        "# Função que gera o gráfico dos resultados de arvores\n",
-        "def plot_results_reg(X, y, regressor, title):     \n",
-        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
-        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "     "
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QaGBc_KwpeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos **dados**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
-        "\n",
-        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].sample(n=60, random_state=0, weights = 'SalePrice')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "68b7c2d6-e1ce-4fbd-fcce-e66c30090c23",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "df.describe()"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.0</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>11633.933333</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>504.683333</td>\n",
-              "      <td>5.783333</td>\n",
-              "      <td>1972.116667</td>\n",
-              "      <td>197536.150000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>8099.879028</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>222.427554</td>\n",
-              "      <td>1.180228</td>\n",
-              "      <td>30.522618</td>\n",
-              "      <td>74910.507145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>8068.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.750000</td>\n",
-              "      <td>142250.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>10510.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>542.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.500000</td>\n",
-              "      <td>188750.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>12010.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>607.250000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>53227.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
-              "count     60.000000      60.0  ...    60.000000      60.000000\n",
-              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
-              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
-              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
-              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
-              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
-              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
-              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 6 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "a128e029-1ad2-43e2-f720-98f9f3cd5670",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>789</th>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1039</th>\n",
-              "      <td>1477</td>\n",
-              "      <td>0</td>\n",
-              "      <td>286</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1970</td>\n",
-              "      <td>RM</td>\n",
-              "      <td>80000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>868</th>\n",
-              "      <td>14762</td>\n",
-              "      <td>0</td>\n",
-              "      <td>672</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1948</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>169000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>782</th>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>611</th>\n",
-              "      <td>10395</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>148000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "789     12205         0         444            8       1966       RL     187500\n",
-              "1039     1477         0         286            4       1970       RM      80000\n",
-              "868     14762         0         672            6       1948       RL     169000\n",
-              "782     16285         0         605            5       2001       RL     187100\n",
-              "611     10395         0         564            6       1978       RL     148000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "10db24ec-8525-476b-d43b-0ea991658a50",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>789</th>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1039</th>\n",
-              "      <td>1477</td>\n",
-              "      <td>0</td>\n",
-              "      <td>286</td>\n",
-              "      <td>4</td>\n",
-              "      <td>1970</td>\n",
-              "      <td>RM</td>\n",
-              "      <td>80000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>868</th>\n",
-              "      <td>14762</td>\n",
-              "      <td>0</td>\n",
-              "      <td>672</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1948</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>169000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>782</th>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>611</th>\n",
-              "      <td>10395</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>6</td>\n",
-              "      <td>1978</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>148000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "789     12205         0         444            8       1966       RL     187500\n",
-              "1039     1477         0         286            4       1970       RM      80000\n",
-              "868     14762         0         672            6       1948       RL     169000\n",
-              "782     16285         0         605            5       2001       RL     187100\n",
-              "611     10395         0         564            6       1978       RL     148000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "77cc56c0-93f3-4e2b-f18f-a891e73ccd0d",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[12205],\n",
-              "       [ 1477],\n",
-              "       [14762],\n",
-              "       [16285],\n",
-              "       [10395]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 7
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "24fde24a-67e1-4cc1-e510-cf6b82248f43",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[187500],\n",
-              "       [ 80000],\n",
-              "       [169000],\n",
-              "       [187100],\n",
-              "       [148000]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yzJ08rJ4J3CS",
-        "colab_type": "text"
-      },
-      "source": [
-        " Criando os subconjuntos de treinamento e testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tVz1mTUOJ4Tb",
-        "colab_type": "code",
-        "outputId": "e6abb0df-30fc-4bf8-9248-002ebcaa7ec9",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n",
-        "\n",
-        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
-        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
-        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Tamanho do Dataset. 60\n",
-            "Tamanho do Conjunto de Treinamento. 48\n",
-            "Tamanho do Conjunto de Testes. 12\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "#X_train = feature_scaling(X_train)\n",
-        "#X_test = feature_scaling(X_test)\n",
-        "\n",
-        "#X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "hDpjk2hPqBar"
-      },
-      "source": [
-        "### Etapa de Treinamento e Validação do Modelo"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "5ZQOFczoqBat"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "84f2ff8a-ab60-4eda-a181-7505c2771d27",
-        "id": "TARpu6xZqBat",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "\n",
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "SAyttRhcqsz_",
-        "colab_type": "text"
-      },
-      "source": [
-        "Transformando as features na quarta ordem polinomial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "OZyCBmL2qs-X",
-        "colab_type": "code",
-        "outputId": "a171e5f2-8798-4ae4-ae51-f25c6094ac5a",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "from sklearn.preprocessing import PolynomialFeatures\n",
-        "\n",
-        "poly_reg = PolynomialFeatures(degree = 4)\n",
-        "X_poly = poly_reg.fit_transform(X)\n",
-        "poly_reg.fit(X_poly, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "PolynomialFeatures(degree=4, include_bias=True, interaction_only=False,\n",
-              "                   order='C')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "92IzIbgrrDdG",
-        "colab_type": "text"
-      },
-      "source": [
-        "Treinando o modelo de regressão polynomial com o conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AiAG4XSPrDy9",
-        "colab_type": "code",
-        "outputId": "2c6ea955-7780-45e6-a656-ccc9d73a5040",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "lin_reg_poly = LinearRegression()\n",
-        "lin_reg_poly.fit(X_poly, y)\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 15
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "5Xy5SEAuqBaz"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "3849351d-be10-4909-dab5-d433d530c716",
-        "id": "ySpZ9RyWqBa0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y_pred = lin_reg_poly.predict(X_poly)\n",
-        "\n",
-        "y_pred[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[210977.15912637],\n",
-              "       [101264.45016613],\n",
-              "       [231210.99529702],\n",
-              "       [242927.64582841],\n",
-              "       [195940.94028164]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "wUrWgrvU0uTq",
-        "colab_type": "text"
-      },
-      "source": [
-        "Avaliando o modelo de regressão linear com a métrica r2:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lETAHBqw0uC5",
-        "colab_type": "code",
-        "outputId": "5e6b148c-5b36-47cd-a50c-0169ef1f2082",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.14645648066355865"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "f8hyc24cqBa7"
-      },
-      "source": [
-        "Avaliando o modelo de regressão polynomial com a métrica r2:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "056cb5e0-7c27-4758-f3da-32562a9d737a",
-        "id": "HDXwQ0ddqBa9",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "lin_reg_poly.score(X_poly, y)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.2824360477057858"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 18
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "gaRZCCjKqBbC"
-      },
-      "source": [
-        "Visualizando os resultados da regressão linear:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "20b55d18-dc2d-46a7-ca33-d5336a6f027f",
-        "id": "GdApU1gYqBbD",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_linear(X, y, regressor, 'Regressão Linear')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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iM4T1CeU18eieezZ/Auztt0NSqakpTpmcKykFHi0kl+RyFjAfuDB+XouxnMVm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\n62UAAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "LlX6EwYwqBbG"
-      },
-      "source": [
-        "Visualizando os resultados da regressão linear polinomial:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "a8a22b70-a788-4185-f249-733db49e81e9",
-        "id": "5GKwud8fqBbG",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 295
-        }
-      },
-      "source": [
-        "plot_results_poly(X, y, lin_reg_poly, poly_reg, 'Regressão Polynomial')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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ptp13hv/9zxJIXnCBKZZNm2w7nT32gKOPhrlzLZXLnDnmHe6Kxam1JForK1uw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\nqrNUdQPwKHCs2LaYhwFPhvZjgOMifY0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l/7pBRJol69SpX7hycZzyXCQiU4EPsER/2wf5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\nTkSuQmCC\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 3027a3e62b06590c5d1cdce68cce93c8879c8c6f Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:07:41 -0300
Subject: [PATCH 11/14] Created using Colaboratory

---
 notebooks/2_semana/Regressao_Multipla.ipynb | 1219 +++++++++++++++++++
 1 file changed, 1219 insertions(+)
 create mode 100644 notebooks/2_semana/Regressao_Multipla.ipynb

diff --git a/notebooks/2_semana/Regressao_Multipla.ipynb b/notebooks/2_semana/Regressao_Multipla.ipynb
new file mode 100644
index 0000000..093cfa3
--- /dev/null
+++ b/notebooks/2_semana/Regressao_Multipla.ipynb
@@ -0,0 +1,1219 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Regressao_Multipla.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "TsV0SiuwpZ7i",
+        "QaGBc_KwpeVt",
+        "VUZAwJjMvh0r"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Multipla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Modelos de Regressão: Regressão Linear Múltipla"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TsV0SiuwpZ7i",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs e funções:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regressão\n",
+        "def plot_results_linear(X, y, regressor, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
+        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()    \n",
+        "    \n",
+        "# Função que gera o gráfico dos resultados de arvores\n",
+        "def plot_results_reg(X, y, regressor, title):     \n",
+        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
+        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
+        "    plt.scatter(X, y, color = 'red')\n",
+        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Tamanho do Lote')\n",
+        "    plt.ylabel('Preço de Vendas')\n",
+        "    plt.show()\n",
+        "     "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QaGBc_KwpeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos **dados**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
+        "\n",
+        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "bcace5e2-035f-4e8b-ed04-d21036a25367",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "df.describe()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "      <td>60.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>11633.933333</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>504.683333</td>\n",
+              "      <td>5.783333</td>\n",
+              "      <td>1972.116667</td>\n",
+              "      <td>197536.150000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>8099.879028</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>222.427554</td>\n",
+              "      <td>1.180228</td>\n",
+              "      <td>30.522618</td>\n",
+              "      <td>74910.507145</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>8068.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.750000</td>\n",
+              "      <td>142250.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10510.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>542.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.500000</td>\n",
+              "      <td>188750.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>12010.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>607.250000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>53227.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
+              "count     60.000000      60.0  ...    60.000000      60.000000\n",
+              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
+              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
+              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
+              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
+              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
+              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
+              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "b06300d2-add2-4c00-f192-52a2e732edf8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 237
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "11e16af0-7111-42a8-fa5a-b813693d64df",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 238
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "fc0ba041-626e-403f-f36e-a0b3fbfe3415",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 8450],\n",
+              "       [ 9600],\n",
+              "       [11250],\n",
+              "       [ 9550],\n",
+              "       [14260]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 239
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "d7c38a52-ee55-478a-9f50-af39542fe538",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[208500],\n",
+              "       [181500],\n",
+              "       [223500],\n",
+              "       [140000],\n",
+              "       [250000]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 240
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "VUZAwJjMvh0r"
+      },
+      "source": [
+        "###  Etapa de Classificação e Validação do Modelo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZOIXAi6Wvpce",
+        "colab_type": "text"
+      },
+      "source": [
+        "Selecionando as principais features do dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "et8CNkI1vpk2",
+        "colab_type": "code",
+        "outputId": "5b82c6a2-0346-4de7-f897-68ec246fd933",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].reindex()\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>208500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>181500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>223500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>140000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
+              "0     8450         0         548            5       2003       RL     208500\n",
+              "1     9600         0         460            8       1976       RL     181500\n",
+              "2    11250         0         608            5       2001       RL     223500\n",
+              "3     9550         0         642            5       1915       RL     140000\n",
+              "4    14260         0         836            5       2000       RL     250000"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 241
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZjC2LJYh1SQ6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
+        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SeWh_rpnYkLd",
+        "colab_type": "code",
+        "outputId": "58ec4049-ab3a-4c72-be4c-921620731399",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "df['MSZoning'].unique()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 242
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eumBvAB8wbq-",
+        "colab_type": "text"
+      },
+      "source": [
+        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rjT1ONG7wb9x",
+        "colab_type": "code",
+        "outputId": "cd492757-3b55-42bc-feb6-43de14ff9341",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "      <th>MSZoning_FV</th>\n",
+              "      <th>MSZoning_RH</th>\n",
+              "      <th>MSZoning_RL</th>\n",
+              "      <th>MSZoning_RM</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>8450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>548</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2003</td>\n",
+              "      <td>208500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>9600</td>\n",
+              "      <td>0</td>\n",
+              "      <td>460</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1976</td>\n",
+              "      <td>181500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>11250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>608</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>223500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>9550</td>\n",
+              "      <td>0</td>\n",
+              "      <td>642</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1915</td>\n",
+              "      <td>140000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>14260</td>\n",
+              "      <td>0</td>\n",
+              "      <td>836</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>250000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
+              "0     8450         0         548  ...            0            1            0\n",
+              "1     9600         0         460  ...            0            1            0\n",
+              "2    11250         0         608  ...            0            1            0\n",
+              "3     9550         0         642  ...            0            1            0\n",
+              "4    14260         0         836  ...            0            1            0\n",
+              "\n",
+              "[5 rows x 10 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 243
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i9fs5x8MvpxV",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "b9uRFW2vvp6b",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
+        "y = df['SalePrice'].values.reshape(-1,1)\n",
+        "\n",
+        "# Dividindo os dados\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "outputId": "8fa737f1-578b-4644-de0f-b1ee66fcc7d0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        }
+      },
+      "source": [
+        "X_train = feature_scaling(X_train)\n",
+        "X_test = feature_scaling(X_test)\n",
+        "\n",
+        "X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.21289571, -0.07099284, -0.86383727,  0.3722173 , -0.45546896,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.26524463, -0.07099284, -0.45626397,  1.26860866,  0.71860895,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.17784146, -0.07099284, -2.25716927,  1.26860866, -1.98829291,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.32447422, -0.07099284, -1.11975539,  1.26860866, -1.10773447,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
+              "       [-0.52903482, -0.07099284, -0.79748813,  0.3722173 , -1.53170705,\n",
+              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 245
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "SMaN0iHNvh0t"
+      },
+      "source": [
+        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "0ce99d9c-c744-4034-fec4-7cdc01565225",
+        "id": "VuzaRxyuvh0u",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 246
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "zT6PUXkWvh0y"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "1da72340-52a8-4022-a2e5-363eb7f76251",
+        "id": "JR-E180Fvh0y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        }
+      },
+      "source": [
+        "y_pred = regressor.predict(X_test)\n",
+        "\n",
+        "y_pred[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[162102.94940288],\n",
+              "       [241746.81922749],\n",
+              "       [119733.72469521],\n",
+              "       [147800.757112  ],\n",
+              "       [285853.71056469]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 247
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "PO6sGI3Gvh01"
+      },
+      "source": [
+        "Avaliando o modelo com a métrica r2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "6f12ba33-16a7-4559-de51-fb0201398258",
+        "id": "fQPgnd8dvh02",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "regressor.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5043465841755175"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 248
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 0836363af15d3257360c6e678c440d720d55bca1 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:07:49 -0300
Subject: [PATCH 12/14] Delete Regressao_Multipla.ipynb

---
 notebooks/2_semana/Regressao_Multipla.ipynb | 1219 -------------------
 1 file changed, 1219 deletions(-)
 delete mode 100644 notebooks/2_semana/Regressao_Multipla.ipynb

diff --git a/notebooks/2_semana/Regressao_Multipla.ipynb b/notebooks/2_semana/Regressao_Multipla.ipynb
deleted file mode 100644
index 093cfa3..0000000
--- a/notebooks/2_semana/Regressao_Multipla.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Regressao_Multipla.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "TsV0SiuwpZ7i",
-        "QaGBc_KwpeVt",
-        "VUZAwJjMvh0r"
-      ],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/2_semana/Regressao_Multipla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Modelos de Regressão: Regressão Linear Múltipla"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TsV0SiuwpZ7i",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs e funções:"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regressão\n",
-        "def plot_results_linear(X, y, regressor, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, regressor.predict(X), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de regerssão polinomial\n",
-        "def plot_results_poly(X, y, lin_reg_poly, poly_reg, title):\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X, lin_reg_poly.predict(poly_reg.fit_transform(X)), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()    \n",
-        "    \n",
-        "# Função que gera o gráfico dos resultados de arvores\n",
-        "def plot_results_reg(X, y, regressor, title):     \n",
-        "    X_grid = np.arange(min(X), max(X), 0.01)\n",
-        "    X_grid = X_grid.reshape((len(X_grid), 1))\n",
-        "    plt.scatter(X, y, color = 'red')\n",
-        "    plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Tamanho do Lote')\n",
-        "    plt.ylabel('Preço de Vendas')\n",
-        "    plt.show()\n",
-        "     "
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QaGBc_KwpeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos **dados**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando o dataset do nosso estudo. O objetivo dos modelos de regressão será de predizer o preço das casas de acordo com diferentes caracteristicas como: localização, área, etc.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
-        "\n",
-        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "bcace5e2-035f-4e8b-ed04-d21036a25367",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "df.describe()"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.0</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "      <td>60.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>11633.933333</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>504.683333</td>\n",
-              "      <td>5.783333</td>\n",
-              "      <td>1972.116667</td>\n",
-              "      <td>197536.150000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>8099.879028</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>222.427554</td>\n",
-              "      <td>1.180228</td>\n",
-              "      <td>30.522618</td>\n",
-              "      <td>74910.507145</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>8068.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.750000</td>\n",
-              "      <td>142250.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>10510.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>542.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.500000</td>\n",
-              "      <td>188750.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>12010.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>607.250000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>53227.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "            LotArea  PoolArea  ...    YearBuilt      SalePrice\n",
-              "count     60.000000      60.0  ...    60.000000      60.000000\n",
-              "mean   11633.933333       0.0  ...  1972.116667  197536.150000\n",
-              "std     8099.879028       0.0  ...    30.522618   74910.507145\n",
-              "min     1477.000000       0.0  ...  1875.000000   80000.000000\n",
-              "25%     8068.500000       0.0  ...  1950.750000  142250.000000\n",
-              "50%    10510.000000       0.0  ...  1976.500000  188750.000000\n",
-              "75%    12010.750000       0.0  ...  2002.000000  235032.000000\n",
-              "max    53227.000000       0.0  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 6 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "b06300d2-add2-4c00-f192-52a2e732edf8",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 237
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "11e16af0-7111-42a8-fa5a-b813693d64df",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 238
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "fc0ba041-626e-403f-f36e-a0b3fbfe3415",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "X = df.loc[:, 'LotArea'].values.reshape(-1,1)\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[ 8450],\n",
-              "       [ 9600],\n",
-              "       [11250],\n",
-              "       [ 9550],\n",
-              "       [14260]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 239
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "d7c38a52-ee55-478a-9f50-af39542fe538",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y = df.loc[:, 'SalePrice'].values.reshape(-1,1)\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[208500],\n",
-              "       [181500],\n",
-              "       [223500],\n",
-              "       [140000],\n",
-              "       [250000]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 240
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "VUZAwJjMvh0r"
-      },
-      "source": [
-        "###  Etapa de Classificação e Validação do Modelo"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZOIXAi6Wvpce",
-        "colab_type": "text"
-      },
-      "source": [
-        "Selecionando as principais features do dataset:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "et8CNkI1vpk2",
-        "colab_type": "code",
-        "outputId": "5b82c6a2-0346-4de7-f897-68ec246fd933",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']].reindex()\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>208500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>181500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>223500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>140000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>250000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  OverallCond  YearBuilt MSZoning  SalePrice\n",
-              "0     8450         0         548            5       2003       RL     208500\n",
-              "1     9600         0         460            8       1976       RL     181500\n",
-              "2    11250         0         608            5       2001       RL     223500\n",
-              "3     9550         0         642            5       1915       RL     140000\n",
-              "4    14260         0         836            5       2000       RL     250000"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 241
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZjC2LJYh1SQ6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Exibir os valores distintos da coluna MSZoning. **Segue legenda:**\n",
-        "FV-Residencial de Vila Flutuante, RH- Residencial de Densidade Alta, RL-Residencial de Densidade Baixa, RM-Residencial de Densidade Média."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "SeWh_rpnYkLd",
-        "colab_type": "code",
-        "outputId": "58ec4049-ab3a-4c72-be4c-921620731399",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "df['MSZoning'].unique()"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 242
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "eumBvAB8wbq-",
-        "colab_type": "text"
-      },
-      "source": [
-        "Codificando as variáveis Categóricos e evitando a armadilha da variável Dummy. **Obs:** Algumas técnicas de aprendizado de máquina exigem que você elimine uma dimensão da apresentação para evitar a dependência entre as variáveis. Isso pode ser feito através do parâmetro \"drop_first = True\"."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rjT1ONG7wb9x",
-        "colab_type": "code",
-        "outputId": "cd492757-3b55-42bc-feb6-43de14ff9341",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = pd.get_dummies(df , columns = ['MSZoning'], drop_first=True)\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "      <th>MSZoning_FV</th>\n",
-              "      <th>MSZoning_RH</th>\n",
-              "      <th>MSZoning_RL</th>\n",
-              "      <th>MSZoning_RM</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>8450</td>\n",
-              "      <td>0</td>\n",
-              "      <td>548</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2003</td>\n",
-              "      <td>208500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>9600</td>\n",
-              "      <td>0</td>\n",
-              "      <td>460</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1976</td>\n",
-              "      <td>181500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>11250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>608</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>223500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>9550</td>\n",
-              "      <td>0</td>\n",
-              "      <td>642</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1915</td>\n",
-              "      <td>140000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>14260</td>\n",
-              "      <td>0</td>\n",
-              "      <td>836</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2000</td>\n",
-              "      <td>250000</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   LotArea  PoolArea  GarageArea  ...  MSZoning_RH  MSZoning_RL  MSZoning_RM\n",
-              "0     8450         0         548  ...            0            1            0\n",
-              "1     9600         0         460  ...            0            1            0\n",
-              "2    11250         0         608  ...            0            1            0\n",
-              "3     9550         0         642  ...            0            1            0\n",
-              "4    14260         0         836  ...            0            1            0\n",
-              "\n",
-              "[5 rows x 10 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 243
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i9fs5x8MvpxV",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis indepedentes e dependentes e dividindo o dataset em conjunto de treinamento e testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "b9uRFW2vvp6b",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "X = df[df.columns[~df.columns.isin(['SalePrice'])]].values\n",
-        "y = df['SalePrice'].values.reshape(-1,1)\n",
-        "\n",
-        "# Dividindo os dados\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "outputId": "8fa737f1-578b-4644-de0f-b1ee66fcc7d0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 187
-        }
-      },
-      "source": [
-        "X_train = feature_scaling(X_train)\n",
-        "X_test = feature_scaling(X_test)\n",
-        "\n",
-        "X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[-0.21289571, -0.07099284, -0.86383727,  0.3722173 , -0.45546896,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.26524463, -0.07099284, -0.45626397,  1.26860866,  0.71860895,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.17784146, -0.07099284, -2.25716927,  1.26860866, -1.98829291,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.32447422, -0.07099284, -1.11975539,  1.26860866, -1.10773447,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078],\n",
-              "       [-0.52903482, -0.07099284, -0.79748813,  0.3722173 , -1.53170705,\n",
-              "        -0.21802209, -0.11405937,  0.5138767 , -0.41556078]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 245
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "SMaN0iHNvh0t"
-      },
-      "source": [
-        "Importando e treinando o modelo de Regressao Linear Multipla com o Conjunto de Treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "0ce99d9c-c744-4034-fec4-7cdc01565225",
-        "id": "VuzaRxyuvh0u",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LinearRegression\n",
-        "\n",
-        "regressor = LinearRegression()\n",
-        "regressor.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 246
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "zT6PUXkWvh0y"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "1da72340-52a8-4022-a2e5-363eb7f76251",
-        "id": "JR-E180Fvh0y",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 102
-        }
-      },
-      "source": [
-        "y_pred = regressor.predict(X_test)\n",
-        "\n",
-        "y_pred[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[162102.94940288],\n",
-              "       [241746.81922749],\n",
-              "       [119733.72469521],\n",
-              "       [147800.757112  ],\n",
-              "       [285853.71056469]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 247
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "PO6sGI3Gvh01"
-      },
-      "source": [
-        "Avaliando o modelo com a métrica r2"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "6f12ba33-16a7-4559-de51-fb0201398258",
-        "id": "fQPgnd8dvh02",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "regressor.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5043465841755175"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 248
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From b65dd2ea051c2e04c055a7939417f6f02e24e9ea Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:21:23 -0300
Subject: [PATCH 13/14] Created using Colaboratory

---
 notebooks/1_semana/Regressao_Logistica.ipynb | 1242 ++++++++++++++++++
 1 file changed, 1242 insertions(+)
 create mode 100644 notebooks/1_semana/Regressao_Logistica.ipynb

diff --git a/notebooks/1_semana/Regressao_Logistica.ipynb b/notebooks/1_semana/Regressao_Logistica.ipynb
new file mode 100644
index 0000000..3e93dd1
--- /dev/null
+++ b/notebooks/1_semana/Regressao_Logistica.ipynb
@@ -0,0 +1,1242 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Regressao_Logistica.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "0KkcOxCxjptF",
+        "gIqtlsYGmOYq"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/1_semana/Regressao_Logistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EB3dPPlgQeVt",
+        "colab_type": "text"
+      },
+      "source": [
+        "#Modelos de Classificação : Regressão Logística"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0KkcOxCxjptF",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Importando libs  e funções"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k6l8c0olEHpN",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando libs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "i7FF5Glb_NrK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import random\n",
+        "import numpy as np\n",
+        "from sklearn.preprocessing import StandardScaler \n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import f1_score\n",
+        "from matplotlib.colors import ListedColormap\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Importando libs de visualização de dados\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns; sns.set()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L7RydZ0qEIl4",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando funções"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxneBJZUEIz4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Função que calcula os reais positivos\n",
+        "def rp(tp, fn):\n",
+        "    return tp + fn\n",
+        "\n",
+        "# Função que calcula os reais negativos     \n",
+        "def rn(fp, tn):\n",
+        "    return fp + tn\n",
+        "\n",
+        "# Função que calcula as predicoes positivas  \n",
+        "def pp(tp, fp):\n",
+        "    return tp + fp\n",
+        "\n",
+        "# Função que calcula as predicoes negativas   \n",
+        "def pn(fn, tn):\n",
+        "    return fn + tn\n",
+        "\n",
+        "# Função que calcula acurácia do modelo\n",
+        "def accuracy (tp, fp, fn, tn):\n",
+        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
+        "     return (accuracy)\n",
+        "    \n",
+        "# Função que calcula a precisão \n",
+        "def precision (tp, fp):\n",
+        "    precision =  (tp / (tp + fp)) #predições positivas\n",
+        "    return precision\n",
+        "\n",
+        "# Função que calcula o recall\n",
+        "def recall(tp, fn):\n",
+        "    recall =  (tp / (tp + fn)) # reais positivos\n",
+        "    return recall\n",
+        "\n",
+        "## Função que calcula o f-measure (media harmonica entre precision e recall)\n",
+        "def f_measure(tp, fp, fn):\n",
+        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
+        "    return f_measure\n",
+        "  \n",
+        "# Função que calcula o Informedness \n",
+        "def informedness(tp, fp, fn, tn):\n",
+        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
+        "    return inform\n",
+        "\n",
+        "# Função que calcula o Markedness\n",
+        "def markdness(tp, fp, fn, tn):    \n",
+        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
+        "    return mark\n",
+        "\n",
+        "# Função de escalonamento\n",
+        "def feature_scaling(data):\n",
+        "    sc = StandardScaler()\n",
+        "    return sc.fit_transform(data)\n",
+        "\n",
+        "# Função que gera o gráfico dos resultados de classificação\n",
+        "def plot_results_class(X, y, classifier, title):\n",
+        "    X_set, y_set = X, y\n",
+        "    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
+        "                         np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
+        "    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
+        "                 alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
+        "    plt.xlim(X1.min(), X1.max())\n",
+        "    plt.ylim(X2.min(), X2.max())\n",
+        "    for i, j in enumerate(np.unique(y_set)):\n",
+        "        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
+        "                    c = ListedColormap(('red', 'green'))(i), label = j)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Idade')\n",
+        "    plt.ylabel('Tarifa')\n",
+        "    plt.legend()\n",
+        "    plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8CLZyvMlh_Qj",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Etapa de exploração e tratamento dos dados"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZfESkLx0EjB8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Importando os dados dos passageiros do titanic e selecionando uma amostragem dos dados para uma melhor visualização. O objetivo dessa tarefa de classificação é prever os passageiros sobreviventes com base nas suas informações pessoais.\n",
+        "\n",
+        "Fonte: [Kaggle](https://www.kaggle.com/c/titanic/data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yq6eIh3NJnuN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
+        "\n",
+        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
+        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g-LzSHX1Jpg1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Descrevendo o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hpLEK45DJomb",
+        "colab_type": "code",
+        "outputId": "c9ce32b0-021e-404c-8e77-78b4975dfa7b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        }
+      },
+      "source": [
+        "# Exporando o dataset\n",
+        "df.info()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 100 entries, 44 to 20\n",
+            "Data columns (total 8 columns):\n",
+            "Unnamed: 0     100 non-null int64\n",
+            "LotArea        100 non-null int64\n",
+            "PoolArea       100 non-null int64\n",
+            "GarageArea     100 non-null int64\n",
+            "OverallCond    100 non-null int64\n",
+            "YearBuilt      100 non-null int64\n",
+            "MSZoning       100 non-null object\n",
+            "SalePrice      100 non-null int64\n",
+            "dtypes: int64(7), object(1)\n",
+            "memory usage: 7.0+ KB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HQOuXFFIi8OZ",
+        "colab_type": "code",
+        "outputId": "6ffbf191-4651-45b5-a166-b8d966ecdfa1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        }
+      },
+      "source": [
+        "# Visualizando o sumário das colunas numéricas do dataset\n",
+        "df.describe()"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>775.810000</td>\n",
+              "      <td>10898.090000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>522.930000</td>\n",
+              "      <td>5.890000</td>\n",
+              "      <td>1970.320000</td>\n",
+              "      <td>199800.560000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>394.243674</td>\n",
+              "      <td>6259.640208</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>233.049232</td>\n",
+              "      <td>1.301863</td>\n",
+              "      <td>31.138297</td>\n",
+              "      <td>76041.565893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>1477.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1875.000000</td>\n",
+              "      <td>80000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>546.750000</td>\n",
+              "      <td>8381.250000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>440.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1950.000000</td>\n",
+              "      <td>148375.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>802.000000</td>\n",
+              "      <td>10760.500000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>551.000000</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>1976.000000</td>\n",
+              "      <td>195000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1039.000000</td>\n",
+              "      <td>12068.750000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>616.500000</td>\n",
+              "      <td>7.000000</td>\n",
+              "      <td>2002.000000</td>\n",
+              "      <td>235032.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1442.000000</td>\n",
+              "      <td>46589.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1014.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>2009.000000</td>\n",
+              "      <td>437154.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        Unnamed: 0       LotArea  ...    YearBuilt      SalePrice\n",
+              "count   100.000000    100.000000  ...   100.000000     100.000000\n",
+              "mean    775.810000  10898.090000  ...  1970.320000  199800.560000\n",
+              "std     394.243674   6259.640208  ...    31.138297   76041.565893\n",
+              "min      26.000000   1477.000000  ...  1875.000000   80000.000000\n",
+              "25%     546.750000   8381.250000  ...  1950.000000  148375.000000\n",
+              "50%     802.000000  10760.500000  ...  1976.000000  195000.000000\n",
+              "75%    1039.000000  12068.750000  ...  2002.000000  235032.000000\n",
+              "max    1442.000000  46589.000000  ...  2009.000000  437154.000000\n",
+              "\n",
+              "[8 rows x 7 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tgh2pe9lJtr6",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizando o dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EvY28fJdJxey",
+        "colab_type": "code",
+        "outputId": "78c50f90-226c-4e9b-daaf-9d7ad88be480",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df.head(5)"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>964</td>\n",
+              "      <td>11316</td>\n",
+              "      <td>0</td>\n",
+              "      <td>510</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>214900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>188</td>\n",
+              "      <td>7018</td>\n",
+              "      <td>0</td>\n",
+              "      <td>528</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1979</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>153337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>153</td>\n",
+              "      <td>13500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>235000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>789</td>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>782</td>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Unnamed: 0  LotArea  PoolArea  ...  YearBuilt  MSZoning  SalePrice\n",
+              "44         964    11316         0  ...       2002        RL     214900\n",
+              "47         188     7018         0  ...       1979        RL     153337\n",
+              "53         153    13500         0  ...       1960        RL     235000\n",
+              "0          789    12205         0  ...       1966        RL     187500\n",
+              "3          782    16285         0  ...       2001        RL     187100\n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiMAqVghJylz",
+        "colab_type": "text"
+      },
+      "source": [
+        "Preenchendo os valores númericos nulos (NA) com a mediana."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KFDodNK4JzlR",
+        "colab_type": "code",
+        "outputId": "d5d62346-ce53-4ac0-e74d-6d447c2d23a5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        }
+      },
+      "source": [
+        "df = df.fillna(df.median())\n",
+        "\n",
+        "df.head(5)"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>LotArea</th>\n",
+              "      <th>PoolArea</th>\n",
+              "      <th>GarageArea</th>\n",
+              "      <th>OverallCond</th>\n",
+              "      <th>YearBuilt</th>\n",
+              "      <th>MSZoning</th>\n",
+              "      <th>SalePrice</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>964</td>\n",
+              "      <td>11316</td>\n",
+              "      <td>0</td>\n",
+              "      <td>510</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2002</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>214900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>188</td>\n",
+              "      <td>7018</td>\n",
+              "      <td>0</td>\n",
+              "      <td>528</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1979</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>153337</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>153</td>\n",
+              "      <td>13500</td>\n",
+              "      <td>0</td>\n",
+              "      <td>564</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>235000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>789</td>\n",
+              "      <td>12205</td>\n",
+              "      <td>0</td>\n",
+              "      <td>444</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1966</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>782</td>\n",
+              "      <td>16285</td>\n",
+              "      <td>0</td>\n",
+              "      <td>605</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2001</td>\n",
+              "      <td>RL</td>\n",
+              "      <td>187100</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Unnamed: 0  LotArea  PoolArea  ...  YearBuilt  MSZoning  SalePrice\n",
+              "44         964    11316         0  ...       2002        RL     214900\n",
+              "47         188     7018         0  ...       1979        RL     153337\n",
+              "53         153    13500         0  ...       1960        RL     235000\n",
+              "0          789    12205         0  ...       1966        RL     187500\n",
+              "3          782    16285         0  ...       2001        RL     187100\n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXWksGyoJ0u3",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis independentes e visualizando as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lJ0y3JijJ1ii",
+        "colab_type": "code",
+        "outputId": "0187a2d8-9a56-42f8-f315-176ec59a1219",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X = df.iloc[:, [5, 9]].values\n",
+        "X[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 26.    ,  14.4583],\n",
+              "       [ 26.    ,   7.55  ],\n",
+              "       [  7.    ,  29.125 ],\n",
+              "       [ 26.    , 146.5208],\n",
+              "       [ 29.    ,  15.2458]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uISYBhKqUqpQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Definindo as variáveis dependentes e visualizando as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okQl0J1SUq9h",
+        "colab_type": "code",
+        "outputId": "42cd730b-3886-48a1-e117-54da7663f8db",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "y = df.iloc[:, 1].values\n",
+        "y[:5]\n"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 0, 1, 1])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yzJ08rJ4J3CS",
+        "colab_type": "text"
+      },
+      "source": [
+        " Criando os subconjuntos de treinamento e testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tVz1mTUOJ4Tb",
+        "colab_type": "code",
+        "outputId": "c97d6fa7-10ea-4bec-c1d6-a67ad0a0b8c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 42)\n",
+        "\n",
+        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
+        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
+        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Tamanho do Dataset. 100\n",
+            "Tamanho do Conjunto de Treinamento. 80\n",
+            "Tamanho do Conjunto de Testes. 20\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OcQtLvcXJ7NT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Normalizando as features e exibindo as 5 primeiras:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aXvlvQ9DJ7i3",
+        "colab_type": "code",
+        "outputId": "1bbd577a-6f18-4d0b-c924-4f4d0baa371e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "X_train = feature_scaling(X_train)\n",
+        "X_test = feature_scaling(X_test)\n",
+        "\n",
+        "X_train[:5]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.2233842 ,  0.62589282],\n",
+              "       [ 0.50330481, -0.63776468],\n",
+              "       [ 0.35796701, -0.68999987],\n",
+              "       [-0.0780464 , -0.55753246],\n",
+              "       [-0.58672871, -0.2466326 ]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jGXuG35WJ_Tj",
+        "colab_type": "text"
+      },
+      "source": [
+        "Treinando o modelo de Árvore de Decisão com o Conjunto de Treinamento"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "gIqtlsYGmOYq"
+      },
+      "source": [
+        "### Etapa de Treinamento e Validação do Modelo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "oF9z-OwymOYs"
+      },
+      "source": [
+        "Importando e treinando o modelo com o conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "8d5f3b06-3372-42a4-fe53-7affbad191ba",
+        "id": "G1uuhHzumOYt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        }
+      },
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "classifier = LogisticRegression(random_state = 0)\n",
+        "classifier.fit(X_train, y_train)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+            "  FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
+              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
+              "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
+              "                   warm_start=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "sKGRDMbWmOY1"
+      },
+      "source": [
+        "Prevendo os resultados do modelo criado com o conjunto de testes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "10be0e36-54f6-4530-8378-9c4b2f72ff8e",
+        "id": "FAvYq8NemOY3",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "y_pred = classifier.predict(X_test)\n",
+        "\n",
+        "y_pred"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "2qVdkDyzmOY_"
+      },
+      "source": [
+        "Criando e exibindo os valores da matriz de confusão com o conjunto de testes "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "412d2fd2-d655-4a5c-c8c0-b906049447ec",
+        "id": "dzm-N1R7mOZD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "source": [
+        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
+        "\n",
+        "confusion_matrix(y_test, y_pred)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[14,  1],\n",
+              "       [ 3,  2]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "Vl2JrA-cmOZI"
+      },
+      "source": [
+        "Visualizando a matriz de confusão"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "b66d506b-fa33-4666-a495-c5587d614bf7",
+        "id": "o8x_wA2CmOZJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        }
+      },
+      "source": [
+        "mat = confusion_matrix(y_test, y_pred)\n",
+        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
+        "plt.xlabel('true label')\n",
+        "plt.ylabel('predicted label');"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAADgCAYAAADlhtpvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBADAYJQAABiMEgAAg1ECAGCw/wOL4LBa8GRKHAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "y6TAt9MomOZL"
+      },
+      "source": [
+        "Visualizando a métrica de **acurácia** utilizando as funções criadas e a bibilioteca sklearn:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "680fcd92-32f2-4554-b847-0b75b5942e39",
+        "id": "rMMCqczomOZM",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "accuracy(tp, fp, fn, tn)\n",
+        "classifier.score(X_test, y_test)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "rAL1ztqJmOZP"
+      },
+      "source": [
+        "Exibindo a medida **f-measure**:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "60cff7ee-505a-4dc2-eb59-73785668ef2f",
+        "id": "dcg2oWJImOZQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "f_measure(tp, fp, fn)\n",
+        "f1_score(y_test, y_pred)  "
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "6SG8SzeXmOZS"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de treinamento:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "8e82d219-2e85-4201-a88e-b4cbecb69c00",
+        "id": "_xENnpTxmOZU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "source": [
+        "plot_results_class(X_train, y_train, classifier, 'Regressão Logística (Conj. de Treinamento)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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tuYJDPYcXX3wRjRs3BgAsWrQIxcXFuHr1Kl5++eUaDY7IUdU9IHpSSYrTackV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\ncgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "colab_type": "text",
+        "id": "QwmncO8mmOZa"
+      },
+      "source": [
+        "Exibindo os resultados do conjunto de testes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "outputId": "62772bbd-82e5-4f3a-fe3c-2f1f92430914",
+        "id": "tA-DeHAMmOZb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 355
+        }
+      },
+      "source": [
+        "plot_results_class(X_test, y_test, classifier, 'Regressão Logística (Conj. de Testes)')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
+            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RmjRpAgBwcnJCcXExWrVqhcuXL1u1OCIikkeF1hyef/55XL58Ge3bt0fbtm0R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\nAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 02ef4dd22cf45c0df61d24a50a8f778a0417cca1 Mon Sep 17 00:00:00 2001
From: Jairo Souza <pto.raphael@gmail.com>
Date: Fri, 22 Nov 2019 02:22:15 -0300
Subject: [PATCH 14/14] Delete Regressao_Logistica.ipynb

---
 notebooks/1_semana/Regressao_Logistica.ipynb | 1242 ------------------
 1 file changed, 1242 deletions(-)
 delete mode 100644 notebooks/1_semana/Regressao_Logistica.ipynb

diff --git a/notebooks/1_semana/Regressao_Logistica.ipynb b/notebooks/1_semana/Regressao_Logistica.ipynb
deleted file mode 100644
index 3e93dd1..0000000
--- a/notebooks/1_semana/Regressao_Logistica.ipynb
+++ /dev/null
@@ -1,1242 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Regressao_Logistica.ipynb",
-      "provenance": [],
-      "collapsed_sections": [
-        "0KkcOxCxjptF",
-        "gIqtlsYGmOYq"
-      ],
-      "toc_visible": true,
-      "include_colab_link": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github",
-        "colab_type": "text"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/r4phael/Python-Deep-Learning-Projects/blob/master/notebooks/1_semana/Regressao_Logistica.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "EB3dPPlgQeVt",
-        "colab_type": "text"
-      },
-      "source": [
-        "#Modelos de Classificação : Regressão Logística"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0KkcOxCxjptF",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Importando libs  e funções"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k6l8c0olEHpN",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando libs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "i7FF5Glb_NrK",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import random\n",
-        "import numpy as np\n",
-        "from sklearn.preprocessing import StandardScaler \n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.metrics import f1_score\n",
-        "from matplotlib.colors import ListedColormap\n",
-        "from sklearn.metrics import confusion_matrix\n",
-        "\n",
-        "# Importando libs de visualização de dados\n",
-        "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns; sns.set()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "L7RydZ0qEIl4",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando funções"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KxneBJZUEIz4",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# Função que calcula os reais positivos\n",
-        "def rp(tp, fn):\n",
-        "    return tp + fn\n",
-        "\n",
-        "# Função que calcula os reais negativos     \n",
-        "def rn(fp, tn):\n",
-        "    return fp + tn\n",
-        "\n",
-        "# Função que calcula as predicoes positivas  \n",
-        "def pp(tp, fp):\n",
-        "    return tp + fp\n",
-        "\n",
-        "# Função que calcula as predicoes negativas   \n",
-        "def pn(fn, tn):\n",
-        "    return fn + tn\n",
-        "\n",
-        "# Função que calcula acurácia do modelo\n",
-        "def accuracy (tp, fp, fn, tn):\n",
-        "     accuracy = ((tp + tn) / (tp + tn + fp + fn))\n",
-        "     return (accuracy)\n",
-        "    \n",
-        "# Função que calcula a precisão \n",
-        "def precision (tp, fp):\n",
-        "    precision =  (tp / (tp + fp)) #predições positivas\n",
-        "    return precision\n",
-        "\n",
-        "# Função que calcula o recall\n",
-        "def recall(tp, fn):\n",
-        "    recall =  (tp / (tp + fn)) # reais positivos\n",
-        "    return recall\n",
-        "\n",
-        "## Função que calcula o f-measure (media harmonica entre precision e recall)\n",
-        "def f_measure(tp, fp, fn):\n",
-        "    f_measure = (2 * precision(tp, fp) * recall(tp, fn)) / (recall(tp, fn) + precision(tp, fp))\n",
-        "    return f_measure\n",
-        "  \n",
-        "# Função que calcula o Informedness \n",
-        "def informedness(tp, fp, fn, tn):\n",
-        "    inform = ((tp/rp(tp, fn)) - (fp/rn(fp, tn)))\n",
-        "    return inform\n",
-        "\n",
-        "# Função que calcula o Markedness\n",
-        "def markdness(tp, fp, fn, tn):    \n",
-        "    mark = ((tp/pp(tp,fp)) - (fn/pn(fn,tn)))\n",
-        "    return mark\n",
-        "\n",
-        "# Função de escalonamento\n",
-        "def feature_scaling(data):\n",
-        "    sc = StandardScaler()\n",
-        "    return sc.fit_transform(data)\n",
-        "\n",
-        "# Função que gera o gráfico dos resultados de classificação\n",
-        "def plot_results_class(X, y, classifier, title):\n",
-        "    X_set, y_set = X, y\n",
-        "    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
-        "                         np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
-        "    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
-        "                 alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
-        "    plt.xlim(X1.min(), X1.max())\n",
-        "    plt.ylim(X2.min(), X2.max())\n",
-        "    for i, j in enumerate(np.unique(y_set)):\n",
-        "        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
-        "                    c = ListedColormap(('red', 'green'))(i), label = j)\n",
-        "    plt.title(title)\n",
-        "    plt.xlabel('Idade')\n",
-        "    plt.ylabel('Tarifa')\n",
-        "    plt.legend()\n",
-        "    plt.show()"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8CLZyvMlh_Qj",
-        "colab_type": "text"
-      },
-      "source": [
-        "### Etapa de exploração e tratamento dos dados"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ZfESkLx0EjB8",
-        "colab_type": "text"
-      },
-      "source": [
-        "Importando os dados dos passageiros do titanic e selecionando uma amostragem dos dados para uma melhor visualização. O objetivo dessa tarefa de classificação é prever os passageiros sobreviventes com base nas suas informações pessoais.\n",
-        "\n",
-        "Fonte: [Kaggle](https://www.kaggle.com/c/titanic/data)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Yq6eIh3NJnuN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "df = pd.read_csv('https://raw.githubusercontent.com/intelligentagents/aprendizagem-supervisionada/master/data/pricing_houses_small.csv')\n",
-        "\n",
-        "#Selecionando uma amostragem dos dados para uma melhor visualização\n",
-        "df = df.loc[:, ['LotArea', 'PoolArea', 'GarageArea', 'OverallCond','YearBuilt', 'MSZoning', 'SalePrice']]"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "g-LzSHX1Jpg1",
-        "colab_type": "text"
-      },
-      "source": [
-        "Descrevendo o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "hpLEK45DJomb",
-        "colab_type": "code",
-        "outputId": "c9ce32b0-021e-404c-8e77-78b4975dfa7b",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 238
-        }
-      },
-      "source": [
-        "# Exporando o dataset\n",
-        "df.info()"
-      ],
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 100 entries, 44 to 20\n",
-            "Data columns (total 8 columns):\n",
-            "Unnamed: 0     100 non-null int64\n",
-            "LotArea        100 non-null int64\n",
-            "PoolArea       100 non-null int64\n",
-            "GarageArea     100 non-null int64\n",
-            "OverallCond    100 non-null int64\n",
-            "YearBuilt      100 non-null int64\n",
-            "MSZoning       100 non-null object\n",
-            "SalePrice      100 non-null int64\n",
-            "dtypes: int64(7), object(1)\n",
-            "memory usage: 7.0+ KB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "HQOuXFFIi8OZ",
-        "colab_type": "code",
-        "outputId": "6ffbf191-4651-45b5-a166-b8d966ecdfa1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 297
-        }
-      },
-      "source": [
-        "# Visualizando o sumário das colunas numéricas do dataset\n",
-        "df.describe()"
-      ],
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Unnamed: 0</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.0</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>775.810000</td>\n",
-              "      <td>10898.090000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>522.930000</td>\n",
-              "      <td>5.890000</td>\n",
-              "      <td>1970.320000</td>\n",
-              "      <td>199800.560000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>394.243674</td>\n",
-              "      <td>6259.640208</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>233.049232</td>\n",
-              "      <td>1.301863</td>\n",
-              "      <td>31.138297</td>\n",
-              "      <td>76041.565893</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>26.000000</td>\n",
-              "      <td>1477.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1875.000000</td>\n",
-              "      <td>80000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>546.750000</td>\n",
-              "      <td>8381.250000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>440.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1950.000000</td>\n",
-              "      <td>148375.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>802.000000</td>\n",
-              "      <td>10760.500000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>551.000000</td>\n",
-              "      <td>5.000000</td>\n",
-              "      <td>1976.000000</td>\n",
-              "      <td>195000.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>1039.000000</td>\n",
-              "      <td>12068.750000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>616.500000</td>\n",
-              "      <td>7.000000</td>\n",
-              "      <td>2002.000000</td>\n",
-              "      <td>235032.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>1442.000000</td>\n",
-              "      <td>46589.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1014.000000</td>\n",
-              "      <td>9.000000</td>\n",
-              "      <td>2009.000000</td>\n",
-              "      <td>437154.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "        Unnamed: 0       LotArea  ...    YearBuilt      SalePrice\n",
-              "count   100.000000    100.000000  ...   100.000000     100.000000\n",
-              "mean    775.810000  10898.090000  ...  1970.320000  199800.560000\n",
-              "std     394.243674   6259.640208  ...    31.138297   76041.565893\n",
-              "min      26.000000   1477.000000  ...  1875.000000   80000.000000\n",
-              "25%     546.750000   8381.250000  ...  1950.000000  148375.000000\n",
-              "50%     802.000000  10760.500000  ...  1976.000000  195000.000000\n",
-              "75%    1039.000000  12068.750000  ...  2002.000000  235032.000000\n",
-              "max    1442.000000  46589.000000  ...  2009.000000  437154.000000\n",
-              "\n",
-              "[8 rows x 7 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 23
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tgh2pe9lJtr6",
-        "colab_type": "text"
-      },
-      "source": [
-        "Visualizando o dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "EvY28fJdJxey",
-        "colab_type": "code",
-        "outputId": "78c50f90-226c-4e9b-daaf-9d7ad88be480",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df.head(5)"
-      ],
-      "execution_count": 20,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Unnamed: 0</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>44</th>\n",
-              "      <td>964</td>\n",
-              "      <td>11316</td>\n",
-              "      <td>0</td>\n",
-              "      <td>510</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2002</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>214900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>47</th>\n",
-              "      <td>188</td>\n",
-              "      <td>7018</td>\n",
-              "      <td>0</td>\n",
-              "      <td>528</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1979</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>153337</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>53</th>\n",
-              "      <td>153</td>\n",
-              "      <td>13500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>7</td>\n",
-              "      <td>1960</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>235000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>789</td>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>782</td>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "    Unnamed: 0  LotArea  PoolArea  ...  YearBuilt  MSZoning  SalePrice\n",
-              "44         964    11316         0  ...       2002        RL     214900\n",
-              "47         188     7018         0  ...       1979        RL     153337\n",
-              "53         153    13500         0  ...       1960        RL     235000\n",
-              "0          789    12205         0  ...       1966        RL     187500\n",
-              "3          782    16285         0  ...       2001        RL     187100\n",
-              "\n",
-              "[5 rows x 8 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 20
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jiMAqVghJylz",
-        "colab_type": "text"
-      },
-      "source": [
-        "Preenchendo os valores númericos nulos (NA) com a mediana."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KFDodNK4JzlR",
-        "colab_type": "code",
-        "outputId": "d5d62346-ce53-4ac0-e74d-6d447c2d23a5",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        }
-      },
-      "source": [
-        "df = df.fillna(df.median())\n",
-        "\n",
-        "df.head(5)"
-      ],
-      "execution_count": 21,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>Unnamed: 0</th>\n",
-              "      <th>LotArea</th>\n",
-              "      <th>PoolArea</th>\n",
-              "      <th>GarageArea</th>\n",
-              "      <th>OverallCond</th>\n",
-              "      <th>YearBuilt</th>\n",
-              "      <th>MSZoning</th>\n",
-              "      <th>SalePrice</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>44</th>\n",
-              "      <td>964</td>\n",
-              "      <td>11316</td>\n",
-              "      <td>0</td>\n",
-              "      <td>510</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2002</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>214900</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>47</th>\n",
-              "      <td>188</td>\n",
-              "      <td>7018</td>\n",
-              "      <td>0</td>\n",
-              "      <td>528</td>\n",
-              "      <td>5</td>\n",
-              "      <td>1979</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>153337</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>53</th>\n",
-              "      <td>153</td>\n",
-              "      <td>13500</td>\n",
-              "      <td>0</td>\n",
-              "      <td>564</td>\n",
-              "      <td>7</td>\n",
-              "      <td>1960</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>235000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>789</td>\n",
-              "      <td>12205</td>\n",
-              "      <td>0</td>\n",
-              "      <td>444</td>\n",
-              "      <td>8</td>\n",
-              "      <td>1966</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>782</td>\n",
-              "      <td>16285</td>\n",
-              "      <td>0</td>\n",
-              "      <td>605</td>\n",
-              "      <td>5</td>\n",
-              "      <td>2001</td>\n",
-              "      <td>RL</td>\n",
-              "      <td>187100</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "    Unnamed: 0  LotArea  PoolArea  ...  YearBuilt  MSZoning  SalePrice\n",
-              "44         964    11316         0  ...       2002        RL     214900\n",
-              "47         188     7018         0  ...       1979        RL     153337\n",
-              "53         153    13500         0  ...       1960        RL     235000\n",
-              "0          789    12205         0  ...       1966        RL     187500\n",
-              "3          782    16285         0  ...       2001        RL     187100\n",
-              "\n",
-              "[5 rows x 8 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 21
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jXWksGyoJ0u3",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis independentes e visualizando as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "lJ0y3JijJ1ii",
-        "colab_type": "code",
-        "outputId": "0187a2d8-9a56-42f8-f315-176ec59a1219",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X = df.iloc[:, [5, 9]].values\n",
-        "X[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[ 26.    ,  14.4583],\n",
-              "       [ 26.    ,   7.55  ],\n",
-              "       [  7.    ,  29.125 ],\n",
-              "       [ 26.    , 146.5208],\n",
-              "       [ 29.    ,  15.2458]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "uISYBhKqUqpQ",
-        "colab_type": "text"
-      },
-      "source": [
-        "Definindo as variáveis dependentes e visualizando as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "okQl0J1SUq9h",
-        "colab_type": "code",
-        "outputId": "42cd730b-3886-48a1-e117-54da7663f8db",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "y = df.iloc[:, 1].values\n",
-        "y[:5]\n"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0, 0, 0, 1, 1])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "yzJ08rJ4J3CS",
-        "colab_type": "text"
-      },
-      "source": [
-        " Criando os subconjuntos de treinamento e testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tVz1mTUOJ4Tb",
-        "colab_type": "code",
-        "outputId": "c97d6fa7-10ea-4bec-c1d6-a67ad0a0b8c7",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state = 42)\n",
-        "\n",
-        "print(\"Tamanho do Dataset. {}\".format(df.shape[0]))\n",
-        "print(\"Tamanho do Conjunto de Treinamento. {}\".format(len(X_train)))\n",
-        "print(\"Tamanho do Conjunto de Testes. {}\".format(len(X_test)))"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Tamanho do Dataset. 100\n",
-            "Tamanho do Conjunto de Treinamento. 80\n",
-            "Tamanho do Conjunto de Testes. 20\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OcQtLvcXJ7NT",
-        "colab_type": "text"
-      },
-      "source": [
-        "Normalizando as features e exibindo as 5 primeiras:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "aXvlvQ9DJ7i3",
-        "colab_type": "code",
-        "outputId": "1bbd577a-6f18-4d0b-c924-4f4d0baa371e",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        }
-      },
-      "source": [
-        "X_train = feature_scaling(X_train)\n",
-        "X_test = feature_scaling(X_test)\n",
-        "\n",
-        "X_train[:5]"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[-0.2233842 ,  0.62589282],\n",
-              "       [ 0.50330481, -0.63776468],\n",
-              "       [ 0.35796701, -0.68999987],\n",
-              "       [-0.0780464 , -0.55753246],\n",
-              "       [-0.58672871, -0.2466326 ]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "jGXuG35WJ_Tj",
-        "colab_type": "text"
-      },
-      "source": [
-        "Treinando o modelo de Árvore de Decisão com o Conjunto de Treinamento"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "gIqtlsYGmOYq"
-      },
-      "source": [
-        "### Etapa de Treinamento e Validação do Modelo"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "oF9z-OwymOYs"
-      },
-      "source": [
-        "Importando e treinando o modelo com o conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "8d5f3b06-3372-42a4-fe53-7affbad191ba",
-        "id": "G1uuhHzumOYt",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 136
-        }
-      },
-      "source": [
-        "from sklearn.linear_model import LogisticRegression\n",
-        "\n",
-        "classifier = LogisticRegression(random_state = 0)\n",
-        "classifier.fit(X_train, y_train)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-            "  FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-              "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
-              "                   warm_start=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "sKGRDMbWmOY1"
-      },
-      "source": [
-        "Prevendo os resultados do modelo criado com o conjunto de testes"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "10be0e36-54f6-4530-8378-9c4b2f72ff8e",
-        "id": "FAvYq8NemOY3",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "y_pred = classifier.predict(X_test)\n",
-        "\n",
-        "y_pred"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 13
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "2qVdkDyzmOY_"
-      },
-      "source": [
-        "Criando e exibindo os valores da matriz de confusão com o conjunto de testes "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "412d2fd2-d655-4a5c-c8c0-b906049447ec",
-        "id": "dzm-N1R7mOZD",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 51
-        }
-      },
-      "source": [
-        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
-        "\n",
-        "confusion_matrix(y_test, y_pred)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[14,  1],\n",
-              "       [ 3,  2]])"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "Vl2JrA-cmOZI"
-      },
-      "source": [
-        "Visualizando a matriz de confusão"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "b66d506b-fa33-4666-a495-c5587d614bf7",
-        "id": "o8x_wA2CmOZJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 241
-        }
-      },
-      "source": [
-        "mat = confusion_matrix(y_test, y_pred)\n",
-        "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False)\n",
-        "plt.xlabel('true label')\n",
-        "plt.ylabel('predicted label');"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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92rB9lfpm93Y6VosW5XQAhF5FpVfz5r+o/v36qk2b1k7HAYIio6JUWV6loYP+TeWfVSqr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\nBADAYJQAABiMEgAAg1ECAGCw/wOL4LBa8GRKHAAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "y6TAt9MomOZL"
-      },
-      "source": [
-        "Visualizando a métrica de **acurácia** utilizando as funções criadas e a bibilioteca sklearn:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "680fcd92-32f2-4554-b847-0b75b5942e39",
-        "id": "rMMCqczomOZM",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "accuracy(tp, fp, fn, tn)\n",
-        "classifier.score(X_test, y_test)"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "rAL1ztqJmOZP"
-      },
-      "source": [
-        "Exibindo a medida **f-measure**:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "60cff7ee-505a-4dc2-eb59-73785668ef2f",
-        "id": "dcg2oWJImOZQ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        }
-      },
-      "source": [
-        "f_measure(tp, fp, fn)\n",
-        "f1_score(y_test, y_pred)  "
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.5"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "6SG8SzeXmOZS"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de treinamento:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "8e82d219-2e85-4201-a88e-b4cbecb69c00",
-        "id": "_xENnpTxmOZU",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 355
-        }
-      },
-      "source": [
-        "plot_results_class(X_train, y_train, classifier, 'Regressão Logística (Conj. de Treinamento)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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tuYJDPYcXX3wRjRs3BgAsWrQIxcXFuHr1Kl5++eUaDY7IUdU9IHpSSYrTackV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\ncgAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "QwmncO8mmOZa"
-      },
-      "source": [
-        "Exibindo os resultados do conjunto de testes:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab_type": "code",
-        "outputId": "62772bbd-82e5-4f3a-fe3c-2f1f92430914",
-        "id": "tA-DeHAMmOZb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 355
-        }
-      },
-      "source": [
-        "plot_results_class(X_test, y_test, classifier, 'Regressão Logística (Conj. de Testes)')"
-      ],
-      "execution_count": 0,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
-            "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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RmjRpAgBwcnJCcXExWrVqhcuXL1u1OCIikkeF1hyef/55XL58Ge3bt0fbtm0R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\nAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file