{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WCPXF7qs5Mbx"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression in R**"
      ],
      "metadata": {
        "id": "OccoYpb-5wgP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "library(ggplot2)\n",
        "data(iris)\n",
        "iris$Species <- factor(ifelse(iris$Species == \"setosa\", \"Setosa\", \"Non-Setosa\"), levels = c(\"Non-Setosa\", \"Setosa\"))\n",
        "log_model <- glm(Species ~ Petal.Length + Petal.Width, data = iris, family = binomial)\n",
        "\n",
        "iris$Predicted <- factor(ifelse(predict(log_model, type = \"response\") > 0.5, \"Setosa\", \"Non-Setosa\"), levels = c(\"Non-Setosa\", \"Setosa\"))\n",
        "\n",
        "ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Predicted, shape = Species)) +\n",
        "  geom_point(size = 3) +\n",
        "  labs(title = \"Logistic Regression Predictions\") +\n",
        "  theme_minimal()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "collapsed": true,
        "id": "usr32_cE5QAW",
        "outputId": "aa8ad346-f9f0-4784-8fa5-90065e64b645"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning message:\n",
            "“glm.fit: algorithm did not converge”\n",
            "Warning message:\n",
            "“glm.fit: fitted probabilities numerically 0 or 1 occurred”\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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Q4AAEAlCHaOTCZTRUWFv6sIOKWlpUVF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93pdJrN5nAPElkqKioURWndunW4B4kgtc7uIVnZ+Hh2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\nEHYAAACS+H8xI69BX6CtPAAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Naive Bayes Classifier**"
      ],
      "metadata": {
        "id": "1RC5funN-Z3e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"e1071\")\n",
        "install.packages(\"caTools\")\n",
        "install.packages(\"caret\")\n",
        "\n",
        "library(e1071)\n",
        "library(caTools)\n",
        "library(caret)\n",
        "data(iris)\n",
        "set.seed(123)\n",
        "split <- sample.split(iris, SplitRatio = 0.7)\n",
        "train_cl <- subset(iris, split == TRUE)\n",
        "test_cl <- subset(iris, split == FALSE)\n",
        "train_scale <- scale(train_cl[, 1:4])\n",
        "test_scale <- scale(test_cl[, 1:4])\n",
        "classifier_cl <- naiveBayes(Species ~ ., data = train_cl)\n",
        "classifier_cl\n",
        "y_pred <- predict(classifier_cl, newdata = test_cl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "collapsed": true,
        "id": "e6e0p0AG5QF2",
        "outputId": "fdd26bf3-1950-4ca2-d979-d32da53f7ea5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘proxy’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘bitops’\n",
            "\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependencies ‘listenv’, ‘parallelly’, ‘future’, ‘globals’, ‘shape’, ‘future.apply’, ‘numDeriv’, ‘progressr’, ‘SQUAREM’, ‘diagram’, ‘lava’, ‘prodlim’, ‘iterators’, ‘clock’, ‘gower’, ‘hardhat’, ‘ipred’, ‘sparsevctrs’, ‘timeDate’, ‘foreach’, ‘ModelMetrics’, ‘plyr’, ‘pROC’, ‘recipes’, ‘reshape2’\n",
            "\n",
            "\n",
            "Loading required package: ggplot2\n",
            "\n",
            "\n",
            "Attaching package: ‘ggplot2’\n",
            "\n",
            "\n",
            "The following object is masked from ‘package:e1071’:\n",
            "\n",
            "    element\n",
            "\n",
            "\n",
            "Loading required package: lattice\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "Naive Bayes Classifier for Discrete Predictors\n",
              "\n",
              "Call:\n",
              "naiveBayes.default(x = X, y = Y, laplace = laplace)\n",
              "\n",
              "A-priori probabilities:\n",
              "Y\n",
              "    setosa versicolor  virginica \n",
              " 0.3333333  0.3333333  0.3333333 \n",
              "\n",
              "Conditional probabilities:\n",
              "            Sepal.Length\n",
              "Y                [,1]      [,2]\n",
              "  setosa     5.013333 0.3224190\n",
              "  versicolor 6.046667 0.5328734\n",
              "  virginica  6.666667 0.6999179\n",
              "\n",
              "            Sepal.Width\n",
              "Y                [,1]      [,2]\n",
              "  setosa     3.426667 0.3956081\n",
              "  versicolor 2.820000 0.3487910\n",
              "  virginica  3.013333 0.3048271\n",
              "\n",
              "            Petal.Length\n",
              "Y                [,1]      [,2]\n",
              "  setosa     1.436667 0.1519604\n",
              "  versicolor 4.330000 0.4235727\n",
              "  virginica  5.576667 0.5922973\n",
              "\n",
              "            Petal.Width\n",
              "Y                 [,1]       [,2]\n",
              "  setosa     0.2466667 0.09371024\n",
              "  versicolor 1.3366667 0.21732438\n",
              "  virginica  2.0500000 0.22244720\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7rMRen3D5QIo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "K5vDjHeGDt9I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"party\")\n",
        "library(party)\n",
        "data(iris)\n",
        "input.data <- iris[1:100, ]\n",
        "output.tree <- ctree(Species ~ Petal.Length + Petal.Width, data = input.data)\n",
        "plot(output.tree)\n",
        "plot(output.tree, type = \"simple\", horizontal = TRUE)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "zBuwjXDl5QLO",
        "outputId": "e7b10811-bd44-4976-941d-70945730c42d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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69R9usX3Bv8d3F/l+IsbC8277MvcjHrZpFBIVmv5Zxl/eBIa2PkX8Fx6o314X8F\nt4f+FRQPD6rHq8udr59V869g7PDq+AE34hL1wu68k0IXRyn7FYR89q+gJ1KbRdhoFX2acdbN\nIpNCsi5Wi34Y/AG3TafQ4/KSnK1780O/unnV/gG3XdPQP3+OP3hPVrNjtjI+/EgjfnjyB9zt\nN4dfZfR4tcYqaWa/CPa+zt3sBdf663G5J1KZxed9fh46/2nN34SybhYZFdKW9l2PCA7vl6HH\nyCtyhtiPEOzfFI0O/aZIXR28uKlj9BdA1qMq7mV/5qjH7TPxcELY16XFh8Gz/1X9LOt2dX3w\n4i2q3P7ASH/9psgTKc2ia/4bwdOPW7SojCzIullkVEjWPUoFh/dFxxZX31Peoehdy3oyp8O0\neWee3DI4vK+6q3F3z+4eeDa60iHq8tCxJWXRJ7e+FS4r+fCsx3Oal157bs4Byy2r6gQ1vHxk\nTm/738LqDv7624UnUprFY7mBkTPGNlc1b7uUdbPIrJCswfbwrHXjOuV1GBl6uP2P3vntx2/t\nFvznytp4abe8VmfXHhdZ83D708iCfQf2ss8aMDzrtdNa5XX+Reh226cWB7pMDP3ub7nK+ne9\nTC6lWVhvnNM+t9XQf9Zcz7pZZEZIhsxRTzZq/Qvz1iS/ERok22axX4W0ve2PG7P6ar8dcZzJ\nsm0W+1VIDX4OTJ389xyYjJZls9i/QrImNepZmS+a2xFk2SzcDmnxBAiLXf6iM4uGMzULt0Ma\ne6jXXyn/OXSsy190ZtFgxmbhekgefdP4mVdfE2YhGfuaEFL6EZJ/EFIGIyT/IKQMRkj+QUgZ\njJD8g5AyGCH5ByFlMELyD0LKYITkH4SUwQjJPwgpgxGSfxBSBiMk/yCkDEZI/kFIGYyQ/IOQ\nMhgh+QchZTBC8g9CymCE5B+ElMEIyT8IKYMRkn8QUgYjJP8gpAxGSP5BSBmMkPyDkDIYIfkH\nIWUwQvIPQspghOQfhJTBCMk/CCmDEZJ/EFIGIyT/IKQMRkj+QUgZjJD8g5AyGCH5ByFlMELy\nD0LKYITkH4SUwQjJP/wT0p5pTQbU82GGJ7n2NWEW2nwT0sr+RQxPk1tfE2ahzy8hbSs8qqKA\n4elx6WvCLFLgl5A2T9ljMTxNLn1NmEUK/BKSjeFpcvFrwiw0EVIGIyT/8HVI8w6uVdBVrnD6\nwY115J7G77Z30hlSslm4YmGjB3zwP9K0q74Oac1DtToWyxUOPHhA4/RS2xq/295JZ0jJZuGK\n33eZ2khdfpemXfV1SE49e8plBw44q3FOJKQEtGfhit8fvqiRehFSHEKSCImQ6kJImgiJkOpC\nSJoIiZDiLS0rK8vtGDz5JsENCElyKaSUZuEKQtI2R0VUJLgBIUkuhZTSLFxBSOYRkuTVH0YJ\nSSKkDEZIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQR\nkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRI\nhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSN\nkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRC\nIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRt\nhCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQR\nEiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFp\nIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIqStk4sDnUo31C74\n8KKOee3OeTPhCoQkGRqekVm4gpCS2d1fnTdrfKDHluiCD4raXHfv7zrmPZ9oDUKSzAzPzCxc\nQUjJ3KRuDJ4uVFOiC0arF4Kn76qTEq1BSJKZ4ZmZhSsIKZm+Rbvss54dqiMLStQe++yAgxKt\nQUiSmeGZmYUrCCmJytwh4XtXayJLxqj3g6dfNzkt0SqEJBkZnqFZuIKQklilwnc7Uy2JLFnZ\n+shXNr4zpNkbiVYhJMnI8AzNwhWElMRyNTF0Pk8tii766AilVPfXYm72fzfUattNboaQDGzE\n0CxcQUhJLFeTQudz1WORJeRQe0MAABRhSURBVCt7dPvjE3/7YcslzpvdOqBWQXu5GUIysBFD\ns3AFISVRocaEzq9Rz0WWHNvs8+Dpzi5d9iRYhYd2kpHhGZqFKwgpid154V+tjlJrwwu25wwO\nnf9CfZBgFUKSjAzP0CxcQUjJlDTbGTzd1zn6YHuT+nHo/GdqWYI1CEkyMzwzs3AFISVzu7o+\neHqLKresyhWrgxd7BD4Onm5tc8CuBGsQkmRmeGZm4QpCSqbqBDW8fGRO7+C/he8r++8Yi5q0\nnXHnrB7qL4nWICTJzPDMzMIVhJTU9qnFgS4TN1vR4VmvndM+r/XQfyVcgZAkQ8MzMgtXEJJ5\nhCTxNApC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJ\nIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC\n0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZI\nEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiER\nkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZC\nkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJ\nkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQR\nkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRI\nhLR1cnGgU+kGx5InT2zRcvCLCVcgJMnQ8IzMwhWElMzu/uq8WeMDPbbULLlTHXLN1Pb5ryZa\ng5AkM8MzMwtXEFIyN6kbg6cL1ZTogq9a9NthWRUtLku0BiFJZoZnZhauIKRk+hbtss96dqiO\nLJinnrbPqhOuQUiSmeGZmYUrCCmJytwh4XtXayJLhhXusXbV911NSJKR4RmahSsIKYlVKny3\nM9WSyJLiI945LkcdclfCVQhJMjI8Q7NwBSElsVxNDJ3PU4siS4qKO015ZH539Xfnza5TDm3k\nZgjJwEYMzcIVhJTEcjUpdD5XPRZZUqDuCZ5uaNGxynGzr5bU6txDboaQDGzE0CxcQUhJVKgx\nofNr1HORJW1zd9pnF6j3EqzCQzvJyPAMzcIVhJTE7ryTQuej1NrIkgG5e+yzy1SiP14QkmRk\neIZm4QpCSqakmf2P3r7O3aILJqk37LNT1boEaxCSZGZ4ZmbhCkJK5nZ1ffD0FlVuWZUrVgcv\nLss5eZdlvd2kT6I1CEkyMzwzs3AFISVTdYIaXj4yp3fw38L3VejvGFeovuUXF+a/mGgNQpLM\nDM/MLFxBSEltn1oc6DJxs1UzvOpbj2za8vS3Eq5ASJKh4RmZhSsIyTxCkngaBSFpIySJkAhJ\nGyFJhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJ\nhERI2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI\n2ghJIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJ\nIiRC0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC\n0kZIEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZI\nEiERkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiER\nkjZCkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERI2ghJIiRC0kZIEiERkjZC\nkgiJkLQRkkRIhKSNkCRCIiRthCQREiFpIySJkAhJGyFJhERIWycXBzqVbohdeKUqTbgCIUmG\nhmdkFq4gpGR291fnzRof6LHFufDtXELSYmZ4ZmbhCkJK5iZ1Y/B0oZriWLa375GEpMXM8MzM\nwhWElEzfol32Wc8O1bXLbsh5ipC0mBmemVm4gpCSqMwdEr53taZm2erCS7cSkhYjwzM0C1cQ\nUhKrVPhuZ6olNcuGdPqWkPQYGZ6hWbiCkJJYriaGzuepRdFFd6lHrPjhvVxWq3UXuRlCMrAR\nQ7NwBSElsVxNCp3PVY9FlnzV5kxLDO+BobWaHSg3Q0gGNmJoFq4gpCQq1JjQ+TXquciSkS3W\nyuE58dBOMjI8Q7NwBSElsTvvpND5KLU2vOBJde369ev/o0atT/SdTUiSkeEZmoUrCCmZkmY7\ng6f7OneLXJ+iosoSrEFIkpnhmZmFKwgpmdvV9cHTW1S5ZVWuWG1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          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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0ciC0LK14ye/zY9BN2qD7vJ9BCsQ0h52tX1ftND0G9OZ05lnidCytOsA/eYHoJ+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\nakFgAAAAAElFTkSuQmCC"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sWJijNdE5QOG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier**"
      ],
      "metadata": {
        "id": "HmBqpUfSF366"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"caret\")\n",
        "install.packages(\"ranger\")\n",
        "library(caret)\n",
        "library(ranger)\n",
        "\n",
        "data(mtcars)\n",
        "mtcars$am <- factor(mtcars$am)\n",
        "\n",
        "set.seed(123)\n",
        "train_index <- createDataPartition(mtcars$am, p = 0.7, list = FALSE)\n",
        "training_set <- mtcars[train_index, ]\n",
        "test_set <- mtcars[-train_index, ]\n",
        "\n",
        "\n",
        "model_rf <- train(am ~ ., data = training_set, method = \"ranger\", importance = \"impurity\")\n",
        "\n",
        "predict_rf <- predict(model_rf, test_set)\n",
        "\n",
        "cm_rf <- confusionMatrix(predict_rf, test_set$am)\n",
        "print(cm_rf)\n",
        "\n",
        "print(varImp(model_rf))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "9HN4L1OXEXUX",
        "outputId": "5f70c8c8-5cb0-409a-a407-0301cfd441a4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "also installing the dependency ‘RcppEigen’\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix and Statistics\n",
            "\n",
            "          Reference\n",
            "Prediction 0 1\n",
            "         0 3 0\n",
            "         1 2 3\n",
            "                                          \n",
            "               Accuracy : 0.75            \n",
            "                 95% CI : (0.3491, 0.9681)\n",
            "    No Information Rate : 0.625           \n",
            "    P-Value [Acc > NIR] : 0.3697          \n",
            "                                          \n",
            "                  Kappa : 0.5294          \n",
            "                                          \n",
            " Mcnemar's Test P-Value : 0.4795          \n",
            "                                          \n",
            "            Sensitivity : 0.600           \n",
            "            Specificity : 1.000           \n",
            "         Pos Pred Value : 1.000           \n",
            "         Neg Pred Value : 0.600           \n",
            "             Prevalence : 0.625           \n",
            "         Detection Rate : 0.375           \n",
            "   Detection Prevalence : 0.375           \n",
            "      Balanced Accuracy : 0.800           \n",
            "                                          \n",
            "       'Positive' Class : 0               \n",
            "                                          \n",
            "ranger variable importance\n",
            "\n",
            "     Overall\n",
            "gear 100.000\n",
            "wt    20.260\n",
            "qsec  19.043\n",
            "drat  18.301\n",
            "disp   6.123\n",
            "cyl    3.658\n",
            "hp     3.569\n",
            "carb   1.983\n",
            "mpg    1.740\n",
            "vs     0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3SfyQNucEXW8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**K-NN Classifier**"
      ],
      "metadata": {
        "id": "Ip7FujcgGC4i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"class\")\n",
        "install.packages(\"caret\")\n",
        "install.packages(\"ggplot2\")\n",
        "\n",
        "library(class)\n",
        "library(caret)\n",
        "library(ggplot2)\n",
        "\n",
        "data(iris)\n",
        "\n",
        "# Select features and target\n",
        "features <- iris[, c(\"Petal.Length\", \"Petal.Width\")]\n",
        "labels <- iris$Species\n",
        "\n",
        "# Split into training and testing sets\n",
        "set.seed(123)\n",
        "train_index <- createDataPartition(labels, p = 0.7, list = FALSE)\n",
        "train_features <- features[train_index, ]\n",
        "test_features <- features[-train_index, ]\n",
        "train_labels <- labels[train_index]\n",
        "test_labels <- labels[-train_index]\n",
        "\n",
        "# Train K-NN and make predictions\n",
        "knn_pred <- knn(train_features, test_features, train_labels, k = 5)\n",
        "\n",
        "# Confusion matrix\n",
        "print(confusionMatrix(knn_pred, test_labels))\n",
        "\n",
        "# Visualize predictions\n",
        "test_data <- cbind(test_features, Predicted = knn_pred)\n",
        "ggplot(test_data, aes(x = Petal.Length, y = Petal.Width, color = Predicted)) +\n",
        "  geom_point(size = 3) +\n",
        "  labs(title = \"K-NN Predictions on Iris Test Data\") +\n",
        "  theme_minimal()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "XXSyvJdHGDBJ",
        "outputId": "9d81c443-8110-4257-deb8-d966f6d2f0ed"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix and Statistics\n",
            "\n",
            "            Reference\n",
            "Prediction   setosa versicolor virginica\n",
            "  setosa         15          0         0\n",
            "  versicolor      0         14         1\n",
            "  virginica       0          1        14\n",
            "\n",
            "Overall Statistics\n",
            "                                          \n",
            "               Accuracy : 0.9556          \n",
            "                 95% CI : (0.8485, 0.9946)\n",
            "    No Information Rate : 0.3333          \n",
            "    P-Value [Acc > NIR] : < 2.2e-16       \n",
            "                                          \n",
            "                  Kappa : 0.9333          \n",
            "                                          \n",
            " Mcnemar's Test P-Value : NA              \n",
            "\n",
            "Statistics by Class:\n",
            "\n",
            "                     Class: setosa Class: versicolor Class: virginica\n",
            "Sensitivity                 1.0000            0.9333           0.9333\n",
            "Specificity                 1.0000            0.9667           0.9667\n",
            "Pos Pred Value              1.0000            0.9333           0.9333\n",
            "Neg Pred Value              1.0000            0.9667           0.9667\n",
            "Prevalence                  0.3333            0.3333           0.3333\n",
            "Detection Rate              0.3333            0.3111           0.3111\n",
            "Detection Prevalence        0.3333            0.3333           0.3333\n",
            "Balanced Accuracy           1.0000            0.9500           0.9500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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MzMWbNmVXb/h1t27S5VewsuufzclezX\n5YfokJWVdd1119n/bTQaMzIy9u/fb7Va69ev/8EHH9ifmGx36623Pvvss4MGDbL/bbBhw4aL\nFy/+97//HTZs2HvvvdegQYOJEyeW36+SrZKTk6s2VgBQ63h11jyPcT6Tqn3u2eTk5JMnTzrv\nobL5aUePHm0frjITFCtvf7k1l2GfU/eZZ54ps9xisbz++uv2R0sFBgYmJyffcsstBw8eLN3m\n4MGDY8aMiY6ODg4O7tChw+uvv24/XtKrVy97gzJz6tpsNpPJ9J///Kdjx44hISFhYWFXXnnl\nli1bSvf51FNPxcXF6fX6rl27VtbDkiVLunbtGhoaqtfrW7duPW/evIsXLzp/R0eOHBGRTp06\nOd7dSy+91KdPn7i4OK1WGxUV1a9fv+XLl1utVidj5ZZdV6jMBMUV9lNmKKr2FhwqnKDYpuBz\nV7LfMh9iefavaGkBAQHx8fGDBg1aunRpUVFRmfbFxcUPPvhgs2bN9Hp9gwYNZsyYkZ2dbbPZ\nbr311rCwsISEhP3795ffr8KtAABKaGyXP78DAAAA/JCar7EDAACoVQh2AAAAKkGwAwAAUAmC\nHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAA\ngEoQ7AAAAFSCYFdWQUFBbm6ur6u4hMlkKikp8XUVl2CUlCgsLGSUXGKUlGCUlPDDUTKbzcXF\nxb6uArVLoK8L8Dtms9lsNvu6iktYrVaLxeLrKi7BKClhMpkYJZfMZrPJZPJ1FZew2WyMkks2\nm83fvt72UbLZbBqNxte1/MkP/8dB9ThiBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAq\nQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbAD\nAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQ\nCYIdAACAShDsAAAAVIJgBwAAoBIEOwBAjWcTyS/R5BYHWKy+LgXwqUBfFwAAQNXlFsvGNNn9\nh+QVR4pIkFbaJ8uoLtIo1teVAb7g7WB34cKF5cuX79u3z2g0Nm3a9LbbbmvZsmWZNjNnzjx+\n/LjjZXBw8AcffODVKgEANcGRM7J0sxSU/L3EaJGf0uWndLkxRYa0811lgI94O9g99thjQUFB\njzzySEhIyKpVqxYuXPjGG28EBweXblNQUHD77benpKTYXwYEcL4YAFDW2Tx5/kspMla8dvV3\nEhkivZp6tybA17yamfLz8+Pj4++8886mTZsmJibecssteXl5GRkZ5ZslJCTE/SUmJsabRQIA\naoT3d1ea6uxWfycGk7eqAfyDV4/YRUREzJs3z/Hy/PnzAQEBcXFxpduYTCaDwbBr16533nkn\nPz+/efPmt9xyS/369b1ZJwDAz+UVS9oJRW16NfNKQYB/8NnNE/n5+UuXLh07dmydOnVKLy8q\nKoqOjjabzTNmzBCR1atXz5s375VXXgkLC6uwH7PZbDAY3FiY1WoVkcLCQjf2WU0Wi8VqtfpV\nSYySEoySEhaLRRglV/xzlCwWiw9LOnRKa7MFu2z22ylT+wSnh/U8yfujpNfrAwO5LbJW09hs\nNu/vNTMz89FHH+3cufP06dM1Go2TlsXFxZMmTZo6deqQIUMqbGAwGPLz8z1TJgDAT+3N1K/e\nE+GyWbcGJTd0LfBCPX4iPDy8zGXrqG18kOv37du3aNGiG2+8ceTIkS4bh4SExMfHZ2dnV9ZA\np9NFRUW5sbyCggKLxeLePqvJZDJZLBa/+r/KKCnBKClRWFhoNpv9apTMZrPZbGaUnDObzSaT\nKSQkxFcFJBY7OyjgUC86yIfj5v1R0mq1XtsX/JO3g92vv/769NNP33fffd26dauwQXp6+ief\nfDJ9+nT7weSSkpJz584lJCRU1mFAQIB7b5u1H0HU6XRu7LOarFarzWbzq5IYJSUYJSX8cJRs\nNpvVavWrkvxzlCwWiw9Lapkk+kAxmF00a5ccoNP5bGoFn48SaiGvBjuj0bhkyZLRo0c3atTI\ncRDOftx406ZNJSUlo0aNiomJ2bVrl9lsnjBhgsViWblyZXh4eJ8+fbxZJwDAzwVp5ao28vnP\nzto0ipXWid4qCPAPXg12Bw8ezMrKWrVq1apVqxwLp02bNmLEiLS0tLy8vFGjRkVERDz66KMr\nVqyYPXu2Tqdr1arVk08+qdfrvVknAMD/jekiB05KxoWK1+p1MqW/OL2KG1Ah39w84c9ycnLM\nZnOZSVh8y2AwmM3myu4L9glGSQlGSYnc3FyTyeRXo2Q0Gk0mE6PknNFoNBqN4eHhvi2joERe\n/Vp+PVV2eVy4zBgkjX09YH4ySqhVuCkaAFBThQfL/amy74Ts/kOOn7NYrJIQre3cUPq2EB13\nEaBWItgBAGq2Tg2lU0PJzS0wmUyxsbHOZ9EC1I3HsAIAAKgEwQ4AAEAlCHYAAAAqQbADAABQ\nCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYId\nAACAShDsAAAAVCLQ1wUAANTjtyxJS5czeRKgkaQ60r2xNIz1dU1AbUKwAwC4QW6xvL5Vfj31\n95Kf0mVDmqQ0k0l9Ra/zXWVAbcKpWABAdeWXyBOfXJLqHL77XRZ/LiaL12sCaiWCHQCgut7e\nKefyK137+1n5aK8XqwFqMYIdAKBasnLlxz9ctNn8ixhMXqkGqN0IdgCAavk503Ubo0V+y/J8\nKUCtR7ADAFTLhUJFzbILPFwHAIIdAKCagpTNr6BnGgbA8wh2AIBqaRijrBkT2gGeR7ADAFRL\n+2SJCHbRJjlGGijLfwCqg2AHAKgWfaBc18NFm5tSvFIKUOsR7AAA1dWvpYzqUvGqQK3c1k9a\nJ3q3IKC24lpWAIAbjOsqzevKuj1yPPvPJRqNtEmUa7tLk3ifVgbUJgQ7AIB7dEiWDslyoVDO\n5UuARupFSmSIr2sCahmCHQDAnWLCJCbM10UAtRXX2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQ\nCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYId\nAACASgT6ugAAgA+YLXLighSUSJheGsaKTnvJ2mKTZF6QYqNEh0pyjARofFSlm5zNkzN5og2Q\nxCipE+bragBPItgBQO1SbJSP98q236TE9OcSfaBc0VLGdpUwvWQXyIc/yJ7jYrb+uTYyRIa2\nk6EdJLAGnuPZc1zW/yQnL/75UiPSrJ5c111aJvi0LMBjCHYAUItcLJLFn8npnEsWGszy1a+y\nP0Ou7yErv5UCwyVr84plzY+yP1PuGSp6nTeLra4PdsvnP1+yxCZy9Iw8tVFu7i0D2/qoLMCT\nauDfXwCAKrHaZOmmsqnO4Vy+vLa1bKpzOJwly7d7rjT323qobKor7Z1dcuCkF6sBvIVgBwC1\nxa6jcjzbWQOL1dnaH/6QI2fcW5GnGEyy9kcXbVZ9JzavFAN4E8EOAGqLXUd934N3/JxZ6aFH\nh9M5ku405gI1EcEOAGqLjAu+78E7Mi66biMimTXk7QDKEewAoLYwWXzfg3eYzIqaGWvI2wGU\nI9gBQG0RG17dHuKq3YN3KHynNeXtAMoR7ACgtujYoLo9dKh2D97RIdl1G32gtEr0fCmAdxHs\nAKC2GNquWhPRxYZLn+buq8aT6kZKSjMXbYa2Fz1zuUJ1CHYAUFtEhcqUfs4adGpY6aqgQLlj\nYNknj/mzib0lIariVTaRlgkyqot3CwK8gmAHALVI9yZyzzCpE1p2eWSI3DlIZg2Rm3pLsK7s\nBG9J0TJvpDSN91aV7hCml3+Nks4VRdX+reTeq2vkE9IAlzgMDQC1S4dkefJ6+SldfsuS/GIJ\nD5YW9aR74z/P0g5uK72ayg9/yB/npNgodcKkXX3p2EACNL6u+/KF62XmEPnjnPyULmfzRBsg\nCVHSo4kkRvu6MsBjCHYAUOsEBUpKs0qvQosIloFtRNp4tyaPaRIvTWrUsUagOjgSDQAAoBIE\nOwAAAJUg2AEAAKgEwQ4AAEAlCHYAAAAqoYa7Ym02m+tGftBnldmL8auS7PyqJEZJCUZJCUZJ\nCUZJCZ+MkkZTA2emgfto/Or/QBUYjcbCwkI3dmixWEREq/Wj6dVtNpvNZgsI8KPDq4ySEv45\nSuJnP/etVqvNZmOUnPPPUfK3/3H2UQoICPCfz877oxQaGqrX6722O/ihGn/ELigoKCgoyI0d\n5uTkmM3mOnXquLHPajIYDGazOSwszNeF/I1RUoJRUiI3N9dkMvnVKBmNRpPJxCg5ZzQajUZj\neHi4rwv5m2OU/CfY+eEoQfX86I8tAAAAVAfBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwA\nAABUosZPdwIA8BNGi+w4LHvT5Vy+aAOkXqT0bCo9m0qAv0w/AqgfwQ4A4AYnzsuLmyW74O8l\np3Mk7YR88bPcNVhimcoN8ApOxQIAqut0jiz69JJU55B+Xp7eKAUlXq8JqJUIdgCA6nprhxQZ\nK12bXSDv7/ZiNUAtRrADAFTLH+fkyBkXbXYdlXwO2gGeR7ADAFTLodOu21htcjjL86UAtR7B\nDgBQLbnFiprlFHm4DgAEOwBANYUGKWoWpvdwHQAIdgCAampW153NAFQHwQ4AUC2tE6VupIs2\nbZMkPsIr1QC1G8EOAFAt2gD5Rx9nDYJ1clNvb1UD1G4EOwBAdbWrL7cPkCBtBavC9DJziCRF\ne70moFbikWIAADdIaSZN4mVDmqSdkEKDiEhUiPRsKsM7SVSIr4sDag2CHQDAPepFypQrRUTy\nSyRAw22wgA8Q7AAAbhYR7OsKgNqKa+wAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUI\ndgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUCfV0A\nAAC1i8EsB05Kdr5oNJIYJa0TJVDr65qgFgQ7AAC8xGqTjfvk0/1iMP29MCJYrukm/Vv7riyo\nCMEOAABvsFrlxa8k7UTZ5fkl8t9vJeOC3NzHF2VBXbjGDgAAb1i/t4JU57DloGw/7MVqoFIE\nOwAAPK6gRL782UWbtT+K1eqVaqBeBDsAADxuX4YYLS7a5BbLkbNeqQbqRbADAMDjzuQqanY6\nx8N1QO0IdgAAeJzN1wWgliDYAQDgcfUiFTVLiPJwHVA7gh0AAB7XsYHrWYgjgqVFPa9UA/Ui\n2AEA4HGRITKknYs2Y7uKll/LqB6+QQAAeMM13aRd/UrX9m0hA9p4sRqoFMEOAABv0AbI7KEy\nspMEXXpONjRIbkyRyVeKxkeFQU14pBgAAF6iDZBrusuwDvLLSTmTKxqN1K8j7ZJEr/N1ZVAL\ngh0AAF4VppdeTX1dBFSKU7EAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwAAAJUg\n2AEAAKgEwQ4AAEAlCHYAAAAqQbADAABQCYIdAACAShDsAAAAVIJgBwAAoBIEOwCo2YwWMVt8\nXQQA/xDo6wIAAFVxvkA+3S970yWnSESkbqT0bCLDOkiY3teVAfAdbwe7CxcuLF++fN++fUaj\nsWnTprfddlvLli3LtCkoKFi2bNn+/ftNJlOrVq2mT59et25dL9cJAP5sz3F5fZsYzX8vOZsn\nG/bJN4fl7sHSjB+ZQG3l7VOxjz32WHZ29iOPPLJkyZK4uLiFCxeWlJSUabNkyZKzZ8/Onz//\nmWeeCQ0NXbhwodVq9XKdAOC3Dp2W17Zekuoc8orluS/kTJ7XawLgH7wa7PLz8+Pj4++8886m\nTZsmJibecssteXl5GRkZpdtkZ2f/8MMPt99+e5MmTZKSkqZPn37y5Mmff/7Zm3UCgN+yWmXl\nt84uqisyyqpdXiwIgD/xarCLiIiYN29egwYN7C/Pnz8fEBAQFxdXus2RI0d0Ol2TJk3sL8PD\nw5OTk3/77Tdv1gkAfutQlmTlumjzS6acL/BKNQD8jM9unsjPz1+6dOnYsWPr1CYTkj0AACAA\nSURBVKlTenleXl5ERIRGo3EsiYqKys2t9MeYxWIxGo1uLMx+2re4uNiNfVaT2Wy2Wq1+VRKj\npASjpIQfjpLFYrFYLH5VUulROpgZKKJz3t4m8mumsXsjD94r68+jVPo3iG95f5R0Ol1gILdF\n1mq++fgzMzMfffTRzp07T5o0qfzay/o/aTabCwsL3VfanzzRZzWZTCZfl1AWo6QEo6QEo6SE\nfZRyC8NcBjsRuZhnLCwsewWz2/nhKBUVFfm6hLK8OUrh4eEEu1rOBx//vn37Fi1adOONN44c\nObL82ujo6Ly8PJvN5oh3ubm5ZY7qlabT6SIiItxYXlFRkcVicW+f1WQymaxWq17vR3MYFBYW\nWq1WRsk5RkkJP/wfZzabLRaL345SbKRWySZ16+gjIlznvyozm81mszk4ONhzu7hc9lEKDw/3\nnyN23h8lUh28/Q349ddfn3766fvuu69bt24VNmjRooXJZPr999+bN28uIva7K9q0aVNZhwEB\nAe79+Ws/Zu5XP9NFxGw2+1VJxcXF/pYPhFFSxt9GqaSkxN9SlD0W+FVJpUepfQP5KM1F+wCN\ntE3WefQdaDQam83mn6PkP8HOD0cJqufVmyeMRuOSJUtGjx7dqFGj7L/YpzvZtGnTJ598IiIx\nMTG9e/d+6aWX/vjjj5MnTz733HPNmjVr27atN+sEAL/VtK40iXfRpmdTiQzxSjUA/IxXj9gd\nPHgwKytr1apVq1atciycNm3aiBEj0tLS8vLyRo0aJSIzZ85ctmzZggULLBZLu3bt/v3vf/vP\nn18A4Fsakdv6yROfSEklF27FhMkNvbxbEwC/obHZbL6uwb/k5OSYzeYyk7D4lsFgMJvNYWFh\nvi7kb4ySEoySErm5uSaTya9GyWg0mkwmPx+l38/KS1/9+TCx0urXkbsHS91Ij5dkNBqNRmN4\neLjH96SYfZRiY2P951iAH44SVI+rLAGg5mlWV568Tr4+JHvT5WyeaDRSv470aCJ9W4jW208U\nAuBHCHYAUCPpdXJ1B7m6g6/rAOBP+MsOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSC\nYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcA\nAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKAS\nBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsA\nAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACV\nINgBAACoBMEOAABAJQh2AAAAKhHo6wIAQP1sIufypMAgoUFSL1I0mkvWWqxyJk9KTBIdKjFh\nl9dzgUGy80UbIHEREqJzY8nOGExy8qKmxKBtGCjhwW7r1vkoAVCCYAcAHmS0yJe/yNe/ysWi\nP5dEhciA1nJ1R9EHSl6xfJwmu45KsfHPtQlRcnUH6dfSdaw5dFrW/yRHzojNJiISGCDtk2Vc\nN2kQ47E3I3I6R9bukX0ZYrboRHQakaZ1ZXQX6ZBcrW5NFvnyF9lSapQiQ2RAa0ntKHp+TQGX\ng/8xAOApBSXy3Jfyx7lLFuYWy0d7ZU+63Jgib2z9O8rYZeXKWzsk7YTMGCiB2kp73rhPPvzx\nkiVmq6SdkAMnZXI/6dXMfe+hlLQT8urXYjT/vcQm8vtZee4LGdFJru1exW4rHKW8Yvl4r/x0\nXO65WuqEVr1moLbhGjsA8AibTV76qmxecci8IM9+XjbVOaSdkHd3Vdrzd7+XTXUOJou8uV2O\nnrnMWhU4cV5e3XJJqitt4z75+mBVurWJvLyl8lG6KEs3icValZ6B2olgBwAesfuY/JblrIHz\nvLLtNzmeXcFyo1ne/97ZhmaLrPpOQX2X6f3dYrQ4a/Dhj1JouOxufzgmh047a3A8W7Yfvuxu\ngVqLYAcAHrHzqEd6+OWk5Ba72PB4tpy8WN29l3a+QA6ectGmyChpJy67ZyWj9O2Ry+4WqLUI\ndgDgEennq91DRUfslHRrc8feS8u4oKhZFXZa4Xss48R5sV12x0AtRbADAI8wmKrdQ0UXtCnp\nVuOOvZdWoqy3Kuy0wvdYhtnCZXaAUgQ7APCIOpc5I115Fc5pp7Dby50Pzy29VWGnSt5OZKgE\n8ssKUIb/KwDgEe2rN7WbiLSvX8FCJTPG6bTSKrG6ey+taV0JDXLdrApvucL3WIU2AOwIdgDg\nEUPbO5uIzqWoUOnbsoLlSdHSuaGLbQe2lWC3PoUiMECGtnfRpnWiNKt72T0PbS86p6OkDZCr\nO1x2t0CtRbADAI+IC5d/9HHWoHm9SlcFamXagEofujCpr7MzmI3jZFxXZSVejuGdpGVCpWsj\nQ2TylVXpNtbVKF3fQ+rXqUrPQO1EsAMAT+nXUm4fIGH6sjd1hujktn4yb4SM7lLB1WMxYXLv\nMGld+bnUqFB5cFTFubBbY5mTKkEeeKhQYIDcM0xSKnqmReM4+ddIiQuvYs9XtJRpAyRMX3Z5\nsE4mXeH6SCGA0nikGAB4UEoz6ZAsu36X305LfomE66VFgvRpLhHBIiJju0rfFvLdUfkjW4qM\nUidM2iVJz2YS5OocbkyYzBspv2TK3nQ5kyfaAEmMkp5Nq3IyVDl9oNw+QIa2l++PSeZ5q9Fs\nS6yj7dRAOjd0/WRb53o1k/blRql3M4kMcVPpQK1BsAMAzwrTy+C2MrhtxWvjI2RUl6p0qxHp\nkKzoXgr3ahwnjePEaDQbjcbw8KoepivH+SgBUIhTsQAAACpBsAMAAFAJgh0AAIBKEOwAAABU\ngmAHAACgEgQ7AAAAldDYbDbXrfyY0WjMz893Y4f2AdFUc1Imd7PZbH5Vkh+Okr8NkTBKivln\nVX7FD79L4n8fHKMkIuHh4Xp9ubmeUZvU+GDndjk5OWazOS4uzteF/M1gMJjN5rCwyh8h5HWM\nkhKMkhK5ubkmk8mvRsloNJpMJkbJOaPR6N557KrPPkqxsbH+k+38cJSgepyKBQAAUAmCHQAA\ngEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoRKCvCwAAVOrgKfn+mGSeDzdbbPWipXND6dlU\ntPxJDqASBDsA8EeFBnl9m+zPsL/SisiJi/LDH/JJmtw5SOrX8WlxAPwVf/cBgN8xmuU/nztS\n3SWycuWpjZKV6/WaANQEBDsA8Dsfp8nx7ErXFhrkzW+EpwYBKI9gBwD+xWiRr3510eb3s3Ik\nyyvVAKhRCHYA4F+OnRWDyXWzA6c8XwqAmoZgBwD+5WKhotOsOYUerwRAjUOwAwD/EhIkGgXN\ngnUerwRAjUOwAwD/0jhONAqSXdN4z5cCoKYh2AGAf4kOlfb1XbSJCJZODb1SDYAahWAHAH7n\nhl6id3qm9YZenIoFUAGCHQD4naRouWuQhARVvPa6HtKnuXcLAlBD8EgxAPBH7erLwnHy0V7Z\n84cUm0REtAHSJknGdJFmdX1dHAB/RbADAD8VGy6T+8mkvpKelV9sMLdoUCeIn9kAnOKHBAD4\nNW2AxIZZTUEWUh0Al7jGDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7\nAAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAA\nlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDY\nAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAA\nqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATB\nDgAAQCUIdgAAACoR6OsCAMAvlFiNr2Wu++DMV78UHDNaTU1D6o+Kv+KeRhPqBcW43PZw0Ynn\n0t/78vzuTMPZCG1ol4iWk5KG35QwNEDj4o9nm9j+d2bLipMb9uQfyjEV1A+OHxzT455GE9qG\nNbGXtCxz/ftnNv+c/7vRamoWmly6pIIS+fKApKXL2TzRBEj9aOnRRK5qI0H8XAdqMY3NZvN1\nDf4lJyfHbDbHxcX5upC/GQwGs9kcFhbm60L+xigpwSgpkZubazKZfD5Kh4tOjNo753DRiTLL\nIwPD3m3/yMj4vk62fSljzb2HXzBaTWWW96vT+cOOT8YHRVe24UVT/vj9D26+8EOZ5TpN4OPN\np4+p22902pzfCiso6Z32C5qZrnhlixQayvYZFyEzh0hyHSf1uofRaDQajeHh4R7fk2L271Js\nbKxGo/F1LX/yw1GC6vkg2J08efK55547evTo+vXrK2wwc+bM48ePO14GBwd/8MEHXiqOX8bK\nMEpKMEpK+EOwyzKe7/n9lIySMxWu1Qfovuz6wpV1Ole4dsWpDZMPPF5Zzz0i23zT49XggKDy\nq0w28+A9d39zMa2ybevoIi6a8itclWTqMOLMq2ZrxYcDI0Pk4TES4+EP2Q8jC8EOEO+fit2+\nffsbb7zRpUuXo0ePVtamoKDg9ttvT0lJsb8MCOBCQAAeNPfIy5WlOhExWE1Tf33ylz7v6jRl\nf2CeM+bM/m2Jk55/yDu45MR7cxvfUn7Va5nrnaQ6Eaks1WkkoEf2nMpSnYjkFcvq7+TOQU76\nBqBa3s5MJpNp8eLFjtBWofz8/ISEhLi/xMS4vsAFAKrmoil/1ekvnbc5XHRi0/nd5Ze/c/rz\nPHOh821fyvjQJhWcGHkpY43yIkurZ+gYY2rhvM1P6ZJTVLXuAdRs3j5iN3DgQBH5/fffK2tg\nMpkMBsOuXbveeeed/Pz85s2b33LLLfXr16+svcViMZnKXtpSHVarVURKSkrc2Gc1mc1mi8Xi\nVyUxSkowSkr4fJS+ufCTyWZ22WzLuR8Hhncts/Dr8z+63DCz5OyhnD+aBCeVXnjOlHOoMP2y\n6nSoV1LxSeHSbDb5NdPUtaGlartQwp+/S/5zKtb7o6TT6bRardd2Bz/kd3dPFRUVRUdHm83m\nGTNmiMjq1avnzZv3yiuvVHZVkNlsLigocHsZnuizmtybX92CUVKCUVLCh6N0sqDSk7ClnS4+\nV77IsyUXlWx7Ivd0vDnykiUlpxSWV57eWundGKVl5xgKYjyeJ/zwu1RY6OIYqvd5c5TCw8MJ\ndrWc3wW7qKiolStXOl4+8MADkyZN2rlz55AhQypsHxgY6N7rUouKiqxWq19d62r/m0+v1/u6\nkL8xSkowSkoUFxdbLBYfjlKSsa6SZokh8eWLrBtcRxSkiIZRieHBl2zbUJ9UWWOXDNpcJc3i\novXh4R78Ce+336WwsDB/O2LnzVHS6XRe2xf8k98FuzJCQkLi4+Ozs7Mra6DVat3710lJSYnV\nag0ODnZjn9VkMBg0Go1flcQoKcEoKWEwGCwWiw9L6h/fTacJdHk29qr4buWLHBDb7ZPz3zrf\nMDm4buvoJhq5JGo0CE5oFdaw/FQmSpzR73XZRiPSNlkXHOzB3/H2+z3987vkP8HOD0cJqud3\nN5ymp6e/+OKLZvOfP2RLSkrOnTuXkJDg26oAqFUdXcSEhIpPCDi0CG0wNLZX+eU3J14dERjq\nfNs7kq8pk+rsZiRfq7zI0s7o918IqnRWAbsujSTaRV3AZViwYIHmUpGRkf3791+7dq0b9zJh\nwgTHcfGUlJTWrVu7sXOXe1QNbx+xu3jxosViyc/PFxH7cbjw8PDg4OBNmzaVlJSMGjUqJiZm\n165dZrN5woQJFotl5cqV4eHhffr08XKdAGqPp1vM2HLhx5OGcxWu1QfolrWdW36uExGpG1Tn\nuZaz//nrE5X13C2y9T2NJlS4anryuP+d2bIjZ19l20brwnNMFVx6aBPrD7GLh5952VLJjCcR\nwXKjs4kHgCqaN29e06ZNRcRqtWZkZKxcufLaa69dsmTJrFmz3L6vCRMmFBcXu2yWlpbWpUsX\nHrVQmreD3Zw5c86ePWv/9+TJk0Xkn//85+jRo9PS0vLy8kaNGhUREfHoo4+uWLFi9uzZOp2u\nVatWTz75pF9dxgFAZRL1cZu6vTAm7YEjRRllVkUEhr7dfv6AOmXvh3WYUn9UoaX4/sNLy5/M\n7Rvd8cNOT4YEVPzjKyhA91HnRdfvf3DLhbK31uo0gQubTx0b33902pwKS3qp000tTAGvfl3B\nkydiw2XmEIlV2zEI+IXRo0eXnq3sgQce6NChw0MPPTRt2jS3n26ePXu2kmbbt293737VwIZL\nXbx48dy5c76u4hIlJSUFBQW+ruISjJISjJISOTk5fjJKRZaS/xxf1eO720I299dtuqLFjuvv\n++2FUyWKajtYcHzqgScbfDMmYFOfiC0Dr/rxzrdObjRbLS43tFgt757+YvCPd0duGRSwqU/y\nN6Nv/eXRn/N/d5T07PHVPb67LXjTleVLyiu2/e8H27/W2KYut92+wrZgnW3jPluJqcoDcHkM\nBkN+fr6XdqaM/btktVp9Xcjf/HCUqmb+/PkismvXrjLL7733XhH5/vvvbTZb3759+/Xr98kn\nnyQnJ/fu3dveYOvWrYMHD46IiAgJCenSpcubb77p2NZqtT7yyCPJycl6vb59+/b/+9//brjh\nhrCwMPvaXr16tWrVytH4yy+/vPLKK8PDw+vVq3f99dcfOXLEZrMNGzbMEWa6detWzT2qhr/f\nPAEA3hESoL+30Y33NrrRaDSaTKbLevBa67BGy9rOrcJOAzQBNyUMvSlhaGUl3dNowj2NJlT4\n4LWIYLmuu1zXvQq7BdwjNDRU/prPRa/XZ2dnz5kzZ968eY0aNRKRr776atiwYX379l21apVe\nr1+7du2UKVMuXrx43333icgzzzwzf/78iRMn3nrrrRcuXHjkkUcqmxdm06ZNw4YNGzJkyKuv\nvmowGB5//PErr7zyp59+Wrp06Zw5cz766KMffvjB/h/WXXus2XydLP0OR1mUYJSUYJSU8J8j\ndg4Gg4FRcskPj0VxxM5zKjtid8UVVwQGBubk5NhstkGDBonI2rVrHWu7dOnSvHnzwsJCx5LR\no0dHREQUFxdbrdakpKT27ds7Vp06dUqn01V4xK579+5NmjQxmf48HP39998HBQU9//zzNptt\nypQppZNMdfaoGn53VywAAPBDFy5cyMrKysrKOn369A8//DBlypQdO3ZMnTo1KirK3iAoKGjk\nyJH2f589e3bv3r0jRowICAgo+cvw4cPz8/N//vnnjIyMU6dO2R9GZZeYmNi9ewXHn8+fP//j\njz+mpqYGBv55jrFnz54Gg2HmzJllWrprjzUdp2IBAIBrI0aMKP0yMDBwxowZzz77rGNJXFyc\nY4bkU6dOicjzzz///PPPl+knMzPTZrOJSHx8fOnlSUlJ+/fvL9P49OnTIlK3ruuJxN21x5qO\nYAcAAFx77rnn7HPLaTSasLCw9u3bR0df8oC78s+9mDx58tSpU8ssbN68eYWPjLdYKni6cUBA\ngPz1IGAlqr/Hmk5RsDt//vy99977+eefZ2dnlx9cG/PHAACgdikpKaWnO3GuYcOGImKxWCrc\nJC8vT0SysrJKLzx+/Hj5lg0aNBCRjIxL5v1JT08PDQ0tc/jNXXus6RQFu+nTp3/44Ye9e/e+\n+uqreQ4dAABwLiYmpmfPnuvXr8/JyXEc2Fu5cuXhw4cXLFjQuHHjuLi4zz//3Gq12o/JHT58\neN++ffbbbEuLiIjo0KHDhg0b8vPzIyIiROTQoUNt2rRZsGDB/Pnz7Y+PM5vNgYGB7tpjTaco\n2H322Wf333//okWLPF0NAABQh0WLFg0ZMqR///733XdfQkLC9u3bn3766YkTJ9pvg7jjjjse\nffTR66+/fuLEiWfPnn3qqae6du166NCh8v08+eSTo0ePHjJkyKxZswoKChYvXly3bt1p06aJ\nSFJSkog88cQT7dq1u/baa921x5pNya2zoaGhH330kUfvzvUfTFGhBKOkBKOkhH9O5MEoueSH\nE3kw3YnnVDbdSWmDBg1q1KhRmYXbt28fMmRIRESETqdr2bLlokWLHLOWmM3muXPnJiQkBAUF\ndejQYd26dXfddVdQUJB9bZkJijdu3JiSkhIaGlq3bt1x48YdPnzYvjwjI6NLly7251RVc4+q\nobEpuEJuyJAhgwYNmju3KtNv1jg5OTlms7nMRKC+ZTAYzGbzZU2X6mmMkhKMkhIVTr3rW1WY\noNjT/HOUjEajXz1A3T5KsbGx9tNz/sAPRwmqp2geu1deeeW9995bv369khQIAAAAn3B2jV3j\nxo3/bBQYaDabx40bFxwcXK9evTLNVHlTCQAAQI3jLNg1b97cyUsAAAD4FWfBbvPmzV6rAwAA\nANWk6Bq77t27Hzx4sPzyDz/8sG3btu4uCQAAAFWhaB67PXv2FBYWllloNpsPHDhQ4TM6AKC2\nWXt261unNmaUnI0IDO0T1eGBJjfHBEb6uigAtY6LYOe4abxHjx4VNujataubKwKAGmVP/qHU\nn+45Z8xxLNl+Me2Z4+9OSx77cps5PiwMQC3kItilpaVt27Zt1qxZY8aMKTOFkkajSUpKKv+o\nXQCoPXZc3DdgzwyLrexDtK1ifSVz7fGSU592ec4nhQGonVwEu06dOnXq1OnTTz995plnWrRo\n4Z2aAKBGsIo1de895VOdw2fZ3y09sebuhtd5syoAtZmimyc+//xzUh0AlPHI0eUFlmLnbR7+\nfZl3igEAcX7ETslTUEwmk8FgcF89AFBjvHd2k8s2Oeb8P4pONwlN9EI9AOAs2I0cOdLx77S0\ntGPHjnXv3j0pKclisRw/fnzfvn1du3bt3bu354sEAH90znhRSbNduT8T7AB4h7Ng995779n/\nsWbNmgMHDqSnpycm/v2z6bfffhs7duzQoUM9WyAA+CuNTdHD5oO0iiaWAoDqU3SN3SOPPPLw\nww+XTnUi0qpVq1mzZj300EOeKQwA/F2D4LpKmvWL7uzpSgDATlGwO3z4cExMTPnlcXFxhw4d\ncndJAFAzTG9wjcs2Sfr4ekEV/PwEAE9QFOzi4uJWrFhRZqHNZluzZk2FgQ8AaoPpyeMSguKc\nt3m1LXMUA/AeRVd+TJ069ZFHHtm/f/9VV10VHx8vIllZWVu2bDl48ODcuXM9XCEA+K/ver3R\ndueEIktJhWtnN5owKq6fl0sCUJspCnbz588PDQ1dsmTJCy+84FgYFxf30EMPzZ8/32O1AYC/\naxRcL/2KdVfvvWdP3iXXpYQFhrzQ8p7J9Uf5qjDAS2w2668/W9L22LJOicmkqRMb0Lqttldf\nCQ72dWW1lMZmsylsarPZMjIysrKybDZbfHx848aNAwIUncmtWXJycsxmc5nnp/mWwWAwm81h\nYWG+LuRvjJISjJISubm5JpPJr0bJaDSaTKbLHaXTJedfO7X2t6KMKG3YqPgrRsT1dWNJ/jlK\nRqNRyXSnXmMfpdjYWMdTzn3OD0fJvWyFheZ3l1t/P1JmuSYsPHDirQHNWvqkqlruMm7C12g0\nDRs2bNiwoeeqAYAaKjE4dkFTnp2N2sRoNL3xku1UZvk1tsIC0/LXdLffFdCoiRcK2bJlS2Rk\nZPfu3b2wL//nLNi1bt160qRJ8+bNa926tZNm3BgLAEBtY/76ywpT3V+rTeYP3gm691+i1Xq6\nkmeffXbkyJEEOztn51Kjo6NDQkLs/3DCW6UCAAD/YLFYdm533sSWfc568EDVun/rrbfatGkT\nEhKSkJAwY8aMkpISEcnKypowYUJSUlJYWFj//v1/+uknERk4cOCnn346e/bsbt26iciZM2du\nvPHGpKSk0NDQvn37fvvtt046/OWXX4YOHRoTExMdHT1s2LCjR49WrVq/4izYrV+/fvbs2SLy\nnVPeKhUAAPgF68lMKSl23ezY4Sp0fuzYscmTJ7/44osFBQU7d+7ctWvXc889JyJjx44VkZ9/\n/jk7O7tfv36pqanFxcVbtmxp2LDhkiVL9uzZIyJjxoy5ePFiWlpadnZ2SkrK8OHDs7OzK+vw\nuuuuS0xMzMjIOHHiRERExKRJk6pQrb9xdio2KSmpS5cuqampqampKSkpWs8fTQUAADVAQZ6S\nVrb8/Cr0nZOTY7PZYmJitFpt06ZNf/zxR61W+9NPP33//ffr1q2LjY0VkYULF7700ksff/zx\nDTfc4Nhw796933///a+//lq3bl0Reeyxx1577bXPPvusXbt25TsUkV27dun1+tDQUBG56aab\nJkyYYLPZ/Ofmm6pxdsRu7Nixx44de/zxx6+44or4+PgJEyb897//PXPmjNeKAwAA/igkREkr\njbJmZXTp0mXatGk9e/bs27fvggULjh07JiKHDx8WkaSkJI1Go9FotFptTk6OfZXD77//HhAQ\n4LgxICQkpFGjRsePH6+wQxHZu3fvyJEjExISEhISpkyZYjKZLBZLFQr2K86C3dq1a7Ozs3ft\n2rVw4cIOHTqsXbv21ltvTUxM7Nat20MPPbRz504VvH8AAHC5ApIaSKDriTU0DatyV6xGo3n1\n1VePHDkyceLE3bt3t23b9v3337df9F9cXGwrZd68ec67slqtRqOxwg6PHj06fPjwIUOGHD9+\nPCsr66233qpCqX7IxUR0Wq02JSXloYce2rZt24ULFzZs2DBz5kyDwfDYY4/17dvXfhjPO4UC\nAAB/oddrO3V13kQTFqZt37EKfZvN5nPnzjVu3HjGjBmffvrptGnTXn755RYtWohIWlqao1mZ\nw3Ui0qJFC6vV+uuvv9pfFhYWpqent2jRosIOf/zxR7PZfP/99wcHB4uIau4ZuIwZhsPDw0eM\nGLFkyZJffvnl999/v/vuu00m0/vvv++54gAAgH/SXj1KExHprMGoayS46DhOnwAAIABJREFU\nKqdiV65c2bVr1z179lit1qysrAMHDrRo0aJt27YDBw687777Tpw4YTKZXnnllQ4dOpw6dUpE\nQkNDjx49mpOT06lTpz59+syZM+f8+fMFBQUPPPBARETE2LFjK+ywcePGFovlu+++MxgMq1ev\n3rlzp4jYO6zRLiPYmUym7du3P/zww3369GnduvXSpUvDwsJuvPFGzxUHAAD8kyYySjflDk10\nnQrXBo66RtulR9V6vvXWW//5z3+OGzcuJCSka9euTZo0Wbx4sYi8++67ycnJHTt2jI2Nfeed\ndz777LOkpCQRsR+B69Chg4isXr06KCiobdu2TZo0OX78+Pbt2yMjIyvsMCUlZc6cOWPGjElK\nSvrqq6/Wr1/frVu3Tp06HT9+vIoj4h9cP1Ls0KFDmzZt2rRp09atW/Pz88PDw/v16zd48ODB\ngwd36NChpt88Uh6PgVKCUVKCUVLCPx+WVYVHinmUf46Svz0si0eK+UZJsfmbLda9P9ounBcR\nCdIHtG4bOHCoJrG+ryurpZxd+Th58uRNmzZlZmbqdLqePXvOnj178ODBvXv31ul0XqsPAAD4\nr+CQwKEjZOgIMRpsBoPzk7PwAmfBbsWKFSKSkpIyY8aMIUOGJCQkeKsqAABQowTpNUF6XxcB\np9fYbdiwYdasWXl5ebfccktiYmL79u1nz569cePGgoICr9UHAAAAhZwdsRsxYsSIESNE5NSp\nU5s2bfryyy9Xr179/PPP63S6Xr16DRkyZPDgwT179gxUMJMNAJRXYCn+4uLuC+a8sZFXJQTF\nlFlrsJpOGs7qNIEJ+lidhp8zAOCa65snSrPZbPv379+8efPWrVu/++677OzsyMjI3Nxcz9Xn\nfVzwrgSjpASj5MRH57ZP+mVhrvnvw/96re6Bhv9Y2HyqiOzJO/TosRVfnP+uxGoUkajA8Gvq\nDni46eTGIYleqI2bJ5Tww9sCuHkCEOdH7MrTaDQdO3YUEa1WGxIS8umnn+blKXpaHAA4TDu4\naFnmujILDRbTo38sX3t264wG19x7+HmD1eRYlWsuWHFqw5qzW97r8OjwuD7eLRYAahKlwS4r\nK2vTpk1ffPHF5s2b7Y+LrVu37jXXXJOamurJ8gCozcuZa8qnOocDhcfuPLS4wlX55qLr9z/4\nbY9lnSNaeKw6AKjZnAW7kpKS7du3f/nll19++eX+/ftFJCAgoGfPnjNmzBg+fHi3bt3853A3\ngJrinkPPV3nbIkvJrN+e3db9FTfWAwBq4izYxcTEFBcXi0h8fPzNN988fPjwYcOGxcSUvcAZ\nABT67Pwuo81cnR6+uZh2tCizeWiyu0oCADVxFuw6deqUmpqampravXt3Ds4BqL43Mj+pfic/\n5B0k2AFAhZwFu127dnmtDgC1wQWzG263Kn0vLQCgNGcTFLv08ssvv/jii+4qBYDqueVIW319\nfPU7AQBVqlawmzlz5t133+2uUgCo3sONb6tmD0EBuivrdHZLMQCgPtWazP2DDz6wWq3uKgWA\n6jUIqZcQFJdlzK5yD1Prj44KZLpXAKhYtYLdNddc4646ANQSu3q+1mzHdVap9Jk34drQAktR\nhavahjV5ovkdHisNAGq8ap2KBYDL1Tgk6btebwRV9OxXjWiebTVrd68324Y1Kb92YEz3r7u/\nFBnoR0/6AuBQZCk5b1LVI0ZrKGdH7Fq3bq2ki0OHDrmpGAC1Qo/ItobB22878Njas1sLLMU2\nmy04IKhHVJuPuzwTpQ0XkX29334va9PH57YfLsoI0gS2CWs8PmHQiLi+vi4cQFk55oL/pK9a\ndfrLY8UnRSQiMHR4XJ95jW/p5E9PiDGbzTqdbtOmTYMHD77crT777LOrr77ac7W5nbNg51cP\nnAagMiva/XtFu39X+Hj7QI325sSrb06sST9MgVpoX/6RUWlzMkrOOJbkm4vez9q87uy2RS3u\nnNXwBh/WVppWq/366687derk60K8wVmw27Fjh/ONCwoKTp8+7dZ6AABADXDKkH31T/dkGc+X\nX2W0mmb/tiRWF+Unf55pNJoBAwb4ugovqdY1dt9//31KSoq7SgEAADXFv46+UmGqc7jnt+er\nNp14SkrKnXfe6Xi5detWrVZ78uTJrKysCRMmJCUlhYWF9e/f/6effhIRi8Wi0WjeeOONJk2a\n3HbbbSLy1ltvtWnTJiQkJCEhYcaMGSUlJWazWaPRbN68WUQyMzPHjRsXHh5uX1tUVCQiZ86c\nufHGG5OSkkJDQ/v27fvtt9+WKanCBuV37Q+UBruNGzfefPPNV1555RV/6d2797hx4wICuP0C\nAIDaJd9c9F7WJudtsk05685uq0LnN91007p16xzzqX3wwQdXXXVV/fr1x44dKyI///xzdnZ2\nv379UlNTi4uLtVqtVqt97bXXPvzwwxdeeOHYsWOTJ09+8cUXCwoKdu7cuWvXrueee65059dc\nc41Opzty5Mj27du/+eabBx74//buPCCqev//+OfMJjvIJiAumHsmamamaabZZu63a2rpLXOJ\nyupWmn0zzRZvZaktNzOv/dRuZqVZtptlWVniQlqaiYriiqisyszZfn9MFwlhGGCYORyfj7+Y\ncz7nM2/eAr7mrFOEEIMHDz59+nRGRkZubm737t1vvPHG3Ny/3JWpwgHl3roG32ld8CqWvfPO\nOzfddNOKFSv279//ww8/7N+/f8uWLT/99FPXrl2XLFlS1yUCAABD2Vb4h1OTqxy2Mf/XGkw+\nYsSInJyc0r1iK1euvPXWW7du3frzzz/PnTs3JiYmODh41qxZLpfro48+cm8yZMiQLl26hIeH\n5+Xl6boeHR1ttVpbtGixefPmadOmlc6ckZGRnp4+e/bsxMTEVq1aLVu27IYbbti2bZt75vj4\n+JCQkKeeekpV1c8+++zcN+txQOlb1+A7rQteBbs5c+Zcf/31p06dys7OtlqtX3zxRWFh4Usv\nvaTreq9eveq6RAAAYCh5SqE3w07LNXk8dKNGjfr27fv+++8LIdavX19YWDh8+PA//vhDCJGU\nlCRJkiRJVqs1Ly9v37597k1atmzp/qJz584TJ07s1q1bz549Z86cWTrALTMzU5KklJSU0sED\nBgzYu3evxWIpvRNIcHBws2bNsrKySrfyPKD0rQ3Cq2D3xx9/3HPPPaVpVNd1m8127733durU\nqWwQBgAAF4JGjmhvhiU4Ymo2/6hRo1atWqXr+ooVKwYPHhweHh4cHCyEOHv2rF5GaQhp0KCB\n+wtJkhYsWLBnz57Ro0dv2rSpffv2K1asKJ1WkiQhhK5XeoN0N03TXC6XlwNK39ogvHryhCzL\nVqvV/XVoaGheXp776+HDh48YMeKVV16pq+q84HK5iouLfTihqqpCiNOnT/twzlpy//h6/iHz\nM7rkDWN2SQhhqC65T6OhS54Zs0u6rsty1Qfj/Ka0S+7/vI3A/10KCQnxT87oEtEm0hZW5bUR\nfaO71mz+YcOG3XXXXRs3bly1atXSpUuFEK1atRJCZGRklF61uW/fvhYtWpTbUFGU06dPN2/e\nPC0tLS0t7Z577vn3v/89fPhw99qWLVvqur5r164OHToIITZt2rRp06ZevXppmrZz586LL75Y\nCFFcXHzgwAH327m1atXK8wBD8SrYtWvX7j//+U/fvn0dDkeTJk2++OIL9xHYU6dO5ecH+DbT\nDofD4XD4cMK8vDxFURo2bOjDOWvJ6XQqihIaaqAb7tMlb9Alb7jvY2eoLrlcLlmW6ZJnLpfL\n5XKFhRnoub2lXTJOsDNgl3zFLtnubjL8mf2ezrNvFdJkQFyPms0fERExYMCAxx9/3GKxXHvt\ntUKI9u3b9+3b98EHH1y+fHliYuKiRYseeuihPXv2JCUlld1w6dKlM2bMWL16defOnXNycn77\n7beyCSw1NfXyyy9/8MEHFyxYIMvyxIkTr7jiinvuuadHjx4PP/zwsmXLGjRoMHXq1PDwcPeF\nGqVbeR5gKF4div3nP//5/vvv33jjjUKIYcOG/etf/5owYcKsWbPS0tIukNv9AQCAsh5NGdup\n8sdLBFkc/+/i6faKHh7opdGjR69bt+6WW26x2f6c5L///W9ycnLHjh1jYmLeeuutzz77rFyq\nE0L84x//uPPOO4cOHRocHNylS5eUlJQ5c+aUHbBmzZrg4OAOHTpceeWV3bp1e/7554UQy5cv\ndzgc7du3T0lJycrK2rBhQ0RERNmtqhxgHFKVR5rd3nnnnaysrEceeeTMmTNDhgxZu3atEKJJ\nkyarVq3q2rWGO1qNyb2XxVBP3TDgXha65A265I0KnzwRWIbdY2e0LhltX5S7SzExMeyx85sT\nrrwROx775tSWcsvjHQ3fvmRWv5oeh0VteBulb7nlFvcXISEhX375ZWZmpizLLVu2tNvtdVYb\nAAAwrjhH1LpLX/4wZ8N/j32xoyizSDl7UUjjAbE9JyYPibSZNs4anFfBrmvXrsuWLWvXrl3p\nEvfFvStXrpw+ffrOnTvrqjoAAGBgkpCGxPceEt870IXgT16dY7dly5bzrzxVFOW3337bu3dv\nHVQFAACAaqtij13pmQqXXXZZhQO6dOni44oAAABQI1UEu4yMjG+//fa+++4bPHhwufN2JUlK\nSkoaP358XZYHAL6Udfbo8mNrNxX8VqyWJDpirom57O+Nrmlgqe25wi5NXpmz/ouTPx11ngy2\nNOga0XZk4rUXBTf2Sc0A4L0qgl1qampqauqnn376/PPPG/ZefABQJV3os/Ytfmb/EleZB1wu\nPfrZ9MyFyzrM6NWwU41n3pS/c9SOx/eePVy65MMT3z25/80Hm4188qKJVsmrM14AwCe8+ovz\n+eeft2rVqqSkJD09/YMPPsjNzRVCKIpSx7UBgM/c+/sLM/cucp332PIDJceu3Xrft6e31Wza\nn/N/u3rL3WVTnZtLk2fvX3rnzmdqNi0A1Iy3HyVfeOGF+Pj4bt26DRs2LDMzUwgxY8aM22+/\nnXgHwPg+PvHDq9krK1tborlG75hxRi2p7rQuTR6543EPG/6/I5+8e3xddacFgBrzKti98cYb\nDz300NVXX71gwYLShW3atHnrrbfmzp1bZ7UBgG88m7XM84DDzhNLjn5a3WlXHP9q/9kjnsfM\n9vjMJQDwLa+C3SuvvDJp0qQPP/xw7NixpQvHjBnz8MMPL1q0qM5qAwAfyFeKfszfUeWwL3J/\nru7Mn+f+VOWYjMI9x1wnqzszANSMV8Hujz/+GD58+PnL+/Tps3//fl+XBAC+dMSZq+lalcMO\nlhyr7syHnDlCVP1UxoNnj1d3ZgCoGa+CXURERElJBSeR5OfnBwcH+7okAPClUKtXf6bCbCE1\nmrnqx5KG2fg7CcBPvAp2HTt2nDNnztmzZ8suPHXq1KxZs7p37143hQGAbzRuEBfvaFjlsM7h\nras7szebRNhCWwYnV3dmAKgZr4Ld//3f/33//fcdO3Z85JFHhBBvvPHGP/7xj5SUlN27dz/+\n+ON1XCEA1IpVsoxOvK7KYbclXl/dmb2Z9paE/o5a3wAZALzkVbDr06fPF198ER4ePn/+fCHE\n4sWLlyxZ0rZt27Vr1/bs2bOOKwSA2no0ZWzjBnEeBoxrPLBrRLvqTts+NOXeJjd7GBDvaDij\nxbjqTgsANVbFkydK9evXb+vWrTk5OUeOHBFCNGvWrGHDqg9tAIARxNqjPu78wo3b/nnUmXv+\n2pvier7S9qGazfxCm8nHXCffO/71+aviHFEfdXo+qUHs+asAoI5UHexKSkq2b98uy3KHDh3i\n4+Pj4+P9UBYA+Fan8Fbbui+ZsfeNt499WaiccS+8KLjxw81vHd94kKWmD/6yS7YVHZ+64cgn\nz2Yt21180L0w1Bo8IqHfrIsmeN5NCJhGUYk4kidkVcSEiYTIQFdzYasi2M2fP3/69OmFhYVC\nCLvdPn78+BdffLFBgwZ+qQ0AfKmRI3pBu6nz2/xz95kDeXJRclBci+DGtZ9WEtLtSTfdnnRT\n1tmjB0uOR9pCW4c2DbbwdxIXhOxTYuVm8eshof3vzj/xEWJgJ9HT14+XVxTFbrevXbv2mmuu\n8cn46k5YX3gKdqtWrbr//vubN28+fvz4kJCQ9evX//vf/7ZYLC+//LLf6gMA32pgsXcMa1kX\nMzcPTmwenFgXMwPGtPWAWPiNcKl/WZhTIP7zndh1RIzrLaSqbwfkLavV+s0336SmpvpqfHUn\nrC88Bbt58+Y1b958x44dYWFh7iXjxo17/fXXn3766YiICL+UBwAAjCj7VAWprtSPmSIuXAzu\n4rO3kySpT58+Phxf3QnrC0+nlWzbtu22224rTXVCiEmTJsmyvGNH1Q/nAQAAJvbupkpTndun\n28Xp4prM3L1797vvvrv05fr1661W64EDByRJ+uqrr1RVlSRp0aJFKSkpt99+uxDil19+SU1N\nDQ4OvvTSS7/55htJkrZv364oinu8pmmSJC1fvvy6665r3759s2bNlixZIoQoHSCEOHTo0NCh\nQ8PCwhISEtLS0s6cOSOE+PXXX6+99tro6OioqKjrrrsuMzOzJt+M33kKdkVFRcnJf7mvpvtl\nUVFR3RYFAAAMrOCs2HmkijGyKjZn1WTyUaNGffDBB5r255MA33333auvvrpx4z/PiLVarVar\n9fXXX1+5cuVLL72kadrAgQMvueSS48ePv/nmmw8//LAQwmI5F28sFovVan3hhReWLVu2c+fO\nxx9/PC0trbj4L5Fz2LBhdrt9z549GzZs+O6776ZMmSKE+Nvf/paYmJidnX3w4MHw8PCxY8fW\n5JvxuyouBCvbGiGEJElCCF2v+tmIAADArA6dFt5kgYMnazL5iBEjcnJyfvjhByGEqqorV668\n9dZby40ZMmRIly5dwsPDf/rpp+zs7CeffDIiIqJjx45paWkVznnbbbe5b+vRr1+/M2fOZGVl\nla7KyMhIT0+fPXt2YmJiq1atli1bdsMNNwghNm7c+Nprr4WGhkZERIwaNSo9Pb1e5B9v72MH\nAADgJiveDfN4rLYyjRo16tu37/vvv9+rV6/169cXFhYOHz683JiWLf+8BOrgwYNWq7V58+bu\nl5deemmFczZt2tT9RVBQkBCi7FNSMzMzJUlKSUlxv+zcuXPnzp2FENu2bXvqqad27twphHA6\nnbIsq6pqsxk9OFVR3759+3766afSl6dOnRJC/P7771FRUaULeVwsAAAXlOiwqscIIWK8G3a+\nUaNGTZ8+fd68eStWrBg8eHB4eLii/CVLlt55Tdd1m80m/e/6W6vVWuGEUuUX6FZ4NDIzM/PG\nG2+cMWPGp59+GhQU9OGHHw4ZMqSG34x/VRHsZs+ePXv27HILH3jggbIv68WeSQAA4CvJ0SI6\nVJyq6tqIjk1qOP+wYcPuuuuujRs3rlq1aunSpR5GJiYmOp3OI0eOJCUlCSG2bNlS3fdq2bKl\nruu7du3q0KGDEGLTpk2bNm2KjY1VFOWhhx6y2+1CiLI7uQzOU7CbMWOG3+oAAAD1hSTEgFSx\n7EdPY1oniDYJNZw/IiJiwIABjz/+uMViufbaaz2M7NGjR2xs7NNPPz1nzpz9+/e//vrr1X2v\n1NTUyy+//MEHH1ywYIEsyxMnTrziiivGjBmjqupPP/3UrVu3VatW/fjjj0KII0eOlB7SNSxP\nwW7mzJn+KgMAANQnfdqKnUfElqyK10YEi/FX1Wr+0aNHDxs27N577/V8WpvD4Xj//ffvueee\nuLi4zp07P/nkk9dcc025Sz+rtGbNmvHjx3fo0CE0NHTo0KHPP/98aGjoww8/PHjwYEmShg4d\nunr16v79+6empm7btq30fD5jkjiQWk5eXp6iKLGxBnput9PpVBQlNDQ00IWcQ5e8QZe8kZ+f\nL8uyobrkcrlkWaZLnrlcLpfLVfZGpwHn7lJMTIyHs6n8zIBd8i1NE+9tFut+E4r2l+UtG4kJ\nfUSsv75vRVE0TXM4HEKIjRs39ujRIz8//4J9koLRL+4AAADGZLGIEd1E33YifZ84dFqUyCI+\nQnRsItolCb+Fa13X27Vrd+WVV86dO/fs2bNPPPFE7969L9hUJwh2AACgNuLCxY2Be+CqJEkr\nV6584IEHmjRpEhQU1Lt37zfeeCNg1RgAwQ4AANRjHTt2XLduXaCrMAqCHVAnDpQc25S/w6m4\nUoPaXhJ2UaDLqUNn1JItBb8fd52Kd0RfGtEm1Boc6IoA4MJFsAN87LvTGY/seXVj/q9/vt4n\nWgQ3nnHRuDGJNwS0Lt87JRc8vnfhm0c+OaOWuJcEWxqMSbrhqZYTY+1RnrcFANSF6l0PDMCz\n1w+tvmrzXedSnRBCiH1nD4/9ddb4nbN1YZ6L0LPOHr1807hXs1eWpjohxFnN+fqh1Zf9fEfm\nmUMBrA0ALlgEO8Bnvj61edKuZytbu+jwR89n/def9dQdWVcGZ0ypLL1lnT06KOPhEs3l56oA\nAAQ7wGce+uNlzwOe3Lc4V87zTzF1avHhj7cXZXoYsKs46/VDH/itHgCAG8EO8I1dxVnbCv/w\nPKZIPftRzvf+qadOvX3sCy/GfOmHSgAAZRHsAN/YUbTXu2GednTVF78W7atyzI5CrxoCAPAh\ngh3gG16eUmaOM8+8+S5cuqzqWpXDAAA+RLADfKN5UKI3w1KCk+q6Ej/w5pttFpRolfgLAwB+\nxZ9dwDeuiOoQY4+sctiA2B5+KKau3RTXs8ox5vhOAaB+IdgBvmGXbI+k3OZ5zPD4qy8Oa+Gf\neurU/U1HhHl8wkSINeih5qP9Vg8AwI1gB/jMA01HetiV1Sqkyevtp/qznrqT2CB2SYfHPQxY\n1P7RpkGN/FYPAMCNYAf4jFWyrEr91wPNbnFY7OVWDYrr9WO3hd4cq60vhsX3+bLL/GZBCeWW\nJwfFf9L5hZEJ/QNSFQBc4HhWLOBLdsn2Yuv7Jjf5+6qc9VtO7ZI1pV1UyqC4XpdGtA10ab7X\nP6bb7z1XfHzi+w15vxxznox3NLwyKnVQfK9gS4NAlwYAFyiCHeB7zYMT/9lsZF5knqIosbGx\ngS6nDgVZHH9r1PdvjfoGuhAAgBAcigUAADANgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDY\nAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAA\nmIQt0AUA8NaOor2vZa/akJeR4zod52h4ZVTHu5KHpYa3CnRdAACjINgB9YCma4/vfePZrGWK\nrrqX5LhO/1a07/VDq6c0v3V2y7ssEnvfAQCBCHaHDx+eO3duZmbm6tWrKxxQVFS0cOHC7du3\ny7Lcpk2bSZMmxcfH+7lIwFAezVzwbNayClc9l/WWrCsvtr7PzyUBAAzI35/yN2zY8OijjyYn\nJ3sYM2/evJycnBkzZjz//PMhISGzZs3SNM1vFQJGs7lgV2Wpzm3ugXc25v/qt3oAAIbl72An\ny/KcOXO6d+9e2YDc3Nz09PQJEyakpKQkJSVNmjTp8OHDO3bs8GeRgKG8dPC9KsfMP7jCD5UA\nAAzO34di+/btK4TYu3dvZQP27Nljt9tTUlLcL8PCwpKTk3fv3p2amlrheE3TZFn2YYW6rgsh\nnE6nD+esJVmWNU0zVEnufaiGKsnEXfrm1JYqx3x9crM3b2TiLvmQoiiqqhqqJLrkjdIuSZIU\n6Fr+5P8u2Ww2q9Xqt7eDARnu4omCgoLw8PCyv5aRkZH5+fmVjZdlubCw0Odl1MWcteRyuQJd\nQnl0yRu171KOfLrKMblyfn5BvpeXUJiySz5Hl7zh28/VPlFUVBToEsrzZ5fCwsIIdhc4wwU7\nIUS1PmzZbLbQ0FAfvvvZs2c1TfPtnLWkKIqmaQ6HI9CFnEOXvOGrLkVZw3K0KrJdpC00PCy8\nyqkM2KWSkhJVVQ31s6SqqqqqdMkzw3YpJCTEOHvs/N8lm82I/63Dnwz3ExAVFVVQUKDreulv\nZn5+fsOGDSsbb7Vag4ODfViA0+nUNM23c9aS0+lUFMVoJdGlKvmqS1c2TF2Vs97zmJ5Rqd68\nkQG75HK5VFU1WkmyLButJAN2yeVyGa0kd5eME+wM2CWYnuHufdWqVStZlktPwisoKMjOzm7X\nrl1gqwICaHzy4CrHTPBiDADA9Pwd7E6fPp2bm+s+UyQ3Nzc3N7ekpEQIsXbt2jVr1gghoqOj\nr7jiildffXX//v3uO95ddNFF7du393OdgHFcH9P9loT+HgYMj796UFwvv9UDADAsfx+Kffjh\nh3Nyctxf33HHHUKIO++8c9CgQRkZGQUFBQMHDhRCTJ48eeHChTNnzlRV9eKLL37ssceMs18d\nCIjFF/+foqvvH//6/FWD43ov6TDd/yUBAAxIct/dA6Xy8vIURYmNjQ10Iee4z4sy1HnTdMkb\nvu2SLvRVOetfzV654XSGoqs2ydozqmNak+E3N+orCW8/+RiwS/n5+bIsG+pnyX2OHV3yzH32\nWFhYWKALOcfdpZiYGOPsCzBgl2B6hrt4AkCFJCENj796ePzVmq7lK8WRtlCeDwsAKIdgB9Qz\nFsnS0F71nU0AABcgPvEDAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcA\nAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGAS\nBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsA\nAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACT\nINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgB\nAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAe00mjEAAAgAElEQVQAAGAS\nBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsA\nAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAExC0nU90DXUiizLxcXF\nPpxQVVVd1202mw/nrCVd13Vdt1gMlMIN2CVN04QQhuqSoihCCLrkmQF/lviN84au65qmWa3W\nQBdyjrtLVqtVkqRA1/In/3cpJCTE4XD47e1gQAb6M1Ezdrs9MjLShxPm5+criuLbOWvJ6XSq\nqhoSEhLoQs6hS96gS94oKCiQZdlQXXK5XIqi0CXPXC6XLMuhoaGBLuSc0i4ZJ9gZsEswvXof\n7IQQdfE7bJy/C+J/xRiqJDdDlUSXvEGXvEGXvGHkLhmnKsN2CSZmoGMNAAAAqA2CHQAAgEkQ\n7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAA\nAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyC\nYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcA\nAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAlboAtAregF+fqpk8JiscQ1EsHB\ngS4HAAAEEsGuvtJ+/01Z+5l+6OCfry0WS+u2tusGSkmNA1oXAAAIGIJdvaR8vkb9Zu1fFmma\n9vtOV+Ye+80jLZ26BqguAAAQSJxjV/+oP/9QPtWVUmT5vbe1g1l+LQgAABgDwa6+KSlRPl/j\naYCiqGtW+qsaAABgIAS7ekb7/Tdx5kwVYw4e0E/k+KceAABgHAS7ekY7ctibYfqRQ3VdCQAA\nMBqCXX0ju7wZpbu8GgYAAMyEYFfPSFFR3g1rWNeVAAAAoyHY1TOW1u2rHtSggSWlRd3XAgAA\njIVgV89IiUmWtlVkO2uvq4XN7p96AACAcRDs6h/b8JFSZKUHZC3NW9j6XufPegAAgEEQ7Oof\nKSLSnvaApXkFB1utXbrZx6UJq9X/VQEAgIDjkWL1khTV0H7X/dqe37XfduinTwrJIjVKtKZ2\n4UGxAABcyAh29ZilVVtLq7aBrgIAABgFh2IBAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAA\nwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQI\ndgAAACZBsAMAADAJgh0AAIBJ2Pz8fkVFRQsXLty+fbssy23atJk0aVJ8fHy5MZMnT87Kyip9\nGRQU9O677/q1SgAAgHrI38Fu3rx5RUVFM2bMaNCgwdtvvz1r1qyXXnrJYvnLjsOioqIJEyZ0\n797d/bLcWgAAAFTIr5kpNzc3PT19woQJKSkpSUlJkyZNOnz48I4dO8oNKywsTEhIiP2f6Oho\nfxYJAABQT/l1j92ePXvsdntKSor7ZVhYWHJy8u7du1NTU0vHyLLsdDo3btz41ltvFRYWtmzZ\ncsyYMY0bN65sTk3TVFX1YZG6rrvL8OGctaSqqqZphiqJLnmDLnmDLnmDLnmjtEuSJAW6lj/5\nv0tWq5XDXBc4vwa7goKC8PDwsr9ykZGR+fn5ZcecOXMmKipKUZS0tDQhxPLly6dNm/baa6+F\nhoZWOKcsy4WFhT4vtVxVRuB0OgNdQnl0yRt0yRt0yRsG7JLL5Qp0CeUVFBQEuoTy/NmlsLCw\noKAgv70dDMjf59hV+UEqMjJy6dKlpS+nTJkyduzYH3/8sX///hWOt1qtwcHBPqzQ6XRqmubb\nOWvJ/ZnPbrcHupBz6JI36JI36JI3jNklVVUdDkegCznH3aWgoCBD7bHzc5dsNn//tw6j8etP\nQFRUVEFBga7rpb91+fn5DRs29LBJcHBwXFxcbm5uZQNsNptvf45lWdY0rbIdhAHhdDoVRTFU\nSXTJG3TJG4qiGK1LLpdLlmVDlWTMLrlcLkOVVNol4wQ7A3YJpufXI/GtWrWSZXnv3r3ulwUF\nBdnZ2e3atSs75sCBA6+88oqiKO6XJSUlJ06cSEhI8GedAAAA9ZFf99hFR0dfccUVr7766uTJ\nkx0Ox6JFiy666KL27dsLIdauXVtSUjJw4MDo6OiNGzcqinLLLbeoqrp06dKwsLAePXr4s04A\nAID6yN/XzkyePLlZs2YzZ86cOnWqw+F47LHH3PvMMzIyNm3aJIQIDw9/8sknT548ef/99z/y\nyCOqqs6ePbtBgwZ+rhMAAKDekdzXh6NUXl6eoiixsbGBLuQcA54XRZe8QZe8kZ+fL8uyobpk\nwHPsjNkll8sVFhYW6ELOcXcpJibGaOfYGapLMD3udgMAAGASBDsAAACTINgBAACYBMEOAADA\nJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2\nAAAAJkGwAwAAMAmCHQAAgEnYAl0APNI0JX2jyNpncTnVJs2s3XuKoBBvtz17VtufqefnCUcD\nS3JTqVFCXRYKAAACj2BnXOrHHyg/fCs0TQhhEUL5dbvy+RpLm3b2sROFxeOuVqdT+ewjddOP\nQlWF0IWQhBBSk2a2QcMtTZv7pXYAABAAHIo1KNfr85UN37hT3Tm60H7f5Zw9QyhKZRvqxcWu\nf7+obtwgVFUI4U51Qgg9+4D8+svajoy6qxkAAAQWwc6I1E9W6/v2Vrq6IN/1xsuVrVTeWaof\nO1rJOlle8ZZ+4nitCwQAAEZEsDMeTVO+/9bzED0rSzt6uIJNM//Q/tjlaUvZpXz5aW2qAwAA\nhkWwMxztl61CU6sapavffV3Bttu3VT3/rl+FLNeoNAAAYGgEO8PRD+7zatjxYxUs9OYwqyzr\np09VtyoAAGB8BDvD0RSt6kFCCL2ivXqad9t6OQwAANQrBDvDkZKbejUsOr6ChTGxVW9ptUrR\n0dWtCgAAGB/BznBsl3YTUtX/LtZefc5faLm4Y5UbWlq2Fo4GNSgMAAAYHMHOeGw2S2onz0Ok\n+EaW5i3OX25pf4nUuInnba3X3Fjz2gAAgIER7IzIPmKMJbryg6qOBo6J91W8SpLst94hhUdU\ntqlt4DBL02a1LhAAABgRwc6QLBb7w49Z2rY7f43UKLHBtBkiLKyyTaXoGPu9D1nathdCCKGf\nWx4ZZb/1DuuVfXxeLAAAMAieFWtUFov99rvE6ZPy+q+1o9m6rFgbJVh79PbmYa9SZJT99kn6\nsaPant/1/DzRIMiS3MTSqo2w2eu+bgAAEDAEO2NrGGMferPT6VQVJSg0tFqbSgmJ1oTEOqoL\nAAAYEIdiAQAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACA\nSRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmYQt0ARc2RZFXr9B/3a6f\nPSuEEJJFiomx3TDY0qGjEEK4nOqP36m/bBU5OTZNdTWMsVx8ibV3Xyk8IrBVAwAAYyLYBYx2\n/Kjy6ou603luka7puSfkZYssbdvZrh8sL1monz51buXJE+p3X6s//2C/ZYyl/SUBqBgAABgb\nh2IDpOSM/MoLf0l1ZWi/75JfnVM21Z3jdMr/fVPbv7duywMAAPUQwS4w5LeXCpfLwwBdVipd\npyjKyuVC03xfFgAAqM8IdoGh7fm9NpvrJ3K0zD98VQwAADAHgl0AaIcO1n5/m5bF0VgAAPAX\nBLtAOHnSB5MUF/tgEgAAYCIEu0CIifPBJGFhPpgEAACYCMEuACzJycJqre0kKS19UgwAADAN\ngl1gWNq0r83mUkKipQXBDgAA/AXBLjDsI8dKQUGeRjjsla6y2W3DRwkL/3YAAOAvCAcB4nA4\nJk+VQkIqXGlJ7eKYPEWKja9gXUiI/R/jLU2b1W15AACgHuKRYoETE+OY/oz66Yfq1nT9zBmh\na8JileIb2QYNtVzURgjheGCq+vOP2vZt2vGjQlGl2DhL+w7WK/tIIaGBLh0AABgRwS6gLBbr\nTUOtNw2teK3Nbu15lbXnVU6nU1GU0FDyHAAA8IRDsQAAACZBsAMAADAJgh0AAIBJEOwAAABM\ngmAHAABgEgQ7AAAAk5B0XQ90DbUiy/KZM2d8OKGiKLqu2+2VP/jB7zRN03XdWuvHy/oQXfIG\nXfKGqqqaphmqS7qu67puMdLDXQz4s6TruqZphvpZcnfJZrNJkhToWv7k/y4FBwc7HA6/vR0M\nqN7fx85ms4WFhflwwoKCAlVVfTtnLblcLlVVg4ODA13IOXTJG3TJG4WFhZqmGapLsiwrimK0\nLimKYrQuybIcUsnjcwKitEvGCXb+75KhPpAgIOp9sJMkybcfhtx/EQz1MdRisRjtkzFd8gZd\n8oYBu6Sqqs//sNQSXfJGaZeME+wM2CWYHtEeAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAA\nAEyi3l8V60N6UaG2c4f94AGrqqhJyZb2HaSYOB/Mq2la5m4ta58oKhRh4ZbmF1lathalV6Sf\nzlU+XKkeypYURQSHWC+9zHrNDeVKEkcOW1RFjU8oV5Kee0Lb9at++qSQLFKjREv7DlJYuA8K\nBgAA9RPBTgghhKYpX32mfvu1UGR3R5St6eLjD6xdutmG3CwaNKj5xPszlZXv6CdySpeoQkhx\njWx/G2lp3sL1+ktiX6b7DtG6EOLsGWXtZ8q6L20jbrN27KR89bn67TqhyEIIixCKEOLjD6xd\nLrMNvlkIoax+T92W/pc3+8huvaqfrd/1gvsYAQBwQSLYCaHr8vIl2vZt569Rt27Sjh12TLq/\nZtlO+/03edl/hKKUf8MTx+U3XpVCQ/T8/Io2U9Xl/0/9vqmefbCiktK1w4eFJPRjR8qvk2X1\nq8/1nOP2Uf8QhrmNEwAA8Bt27Qj1h28rTHVu+pHDykfv12BavahQXr7k/FT3J0WuONW5txWi\nwlT359pjRypIdf+jbd+m/rihOpUCAACTuOCDnaqqX3/h+XG56uaf9ZO51Z74u29Eydka1+VJ\nVTvj1HWfC02rk7cGAAAGdqEHO+3gfr24uMrDltquX6s9864dVUewuqEXF2kH9gfkrQEAQABd\n6MFOP3XKu2HV3mPn5cwVbVnD7f4yR/V3MQIAgPruQg92ktWLDuhCWKt/lUmNL02tcjefF8lP\nsvHMaQAALjgXfLCLT/RikJAaJVR75upv4vXU3ry7F98XAAAwlws+2CUmSXHxVQyyOyztLqnu\nzNbULjWsqdakuEZSQlKg3h0AAATKhR7shCTZBgzxPMR2dX8pNLS6E1u7XynFxNa0rBrThRC2\nm4ZwHzsAAC5AF3ywE8LSroPthoGVru10qbXvtTWZ1263j50ghUdUuFKKiLS2aV/ptpJku/Jq\nDyVZ3LsDKzjZTrLdMMjS9uLqFgsAAEyAJ08IIYS1T3+pUaLyyYf6ieOlC6WwcOs111u7X1nj\nvV9SowT75IeVT1Zr27edu7GcxWJJ7WIbMEQKj5DWr1O/WKOXu+dcRGSDex4SkZFSy1aVliSE\nmnKR+tXnelHhubXxjWwDhpDqAAC4YEm67ou7a5iFfvRI0b5MVZYjmqdYmjb31UNX9eJi/eB+\nvbBQCg+XmqaUO7CrZmzVf9msFZ+1xMbZrrteREaXK8mZfUBTlKCkxuVL0jTtYJZ+MldYLJaE\nJCnRf+fV5eXlKYoSG+v/Y82VcjqdiqKEVv+ged2hS97Iz8+XZdlQXXK5XLIs0yXPXC6Xy+UK\nCwsLdCHnuLsUExMjGeZcFAN2CabHHru/kBKT1OAQRVEsPv0DKoWGSu06VLbW2qmL6FTplRZS\nYpIUHaMriuX8/2YsFkvzFqJ5C1/VCQAA6jXOsQMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7\nAAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAA\nk7AFugAIoWl6fp5wlkgRUSIkJNDVAACA+opgF0j6mWL16y+1rel6cZEQQkiS1LiJ7ap+lo6d\nA10aAACofwh2AaMfPyovXqDnnS6zSNcPHZT/+6Z116+2m0cLCwfKAQBANRAdAkM/U1w+1ZWh\nbk1XPvvIzyUBAID6jmAXGOrXX1aW6v4c8MO3es5xv9UDAABMgGAXCJqmbU2vYoyqqls2+aUa\nAABgEgS7ANDzTv95tYTnYYcP+qEYAABgGgS7QHCWeDNKL/FqGAAAgBvBLhAiorwZJUV6NQwA\nAMCNYBcAUmiolJRc5TBL67Z+KAYAAJgGwS4wbFf19TxACo+wdr7MP8UAAABzINgFhiX1Ukun\nSytdbbPZbhkjHA4/VgQAAOo9gl2ASJJ9xG3WnldVsCY8wv6PiZaWrf1fFAAAqNd4pFjgWCy2\nQcOt3a5Qt6br2Qd1l1OKiLS0aWft0o19dQAAoAYIdgEmJSTZbhwc6CoAAIAZcCgWAADAJAh2\nAAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAA\nJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYhM3P71dUVLRw4cLt27fL\nstymTZtJkybFx8fXYAwAAADK8fceu3nz5uXk5MyYMeP5558PCQmZNWuWpmk1GAMAAIBy/Brs\ncnNz09PTJ0yYkJKSkpSUNGnSpMOHD+/YsaO6YwAAAHA+vx6K3bNnj91uT0lJcb8MCwtLTk7e\nvXt3ampqtcaUpeu6b/fn6bouhFBV1Ydz1pKmaZqmGaokuuQNuuQNuuQNY3ZJ13VDlVTaJUmS\nAl3Ln/zfJYvFYpxvHwHh12BXUFAQHh5e9mcuMjIyPz+/umPKcrlchYWFPi/19OnTPp+zlpxO\nZ6BLKI8ueYMueYMueYMueSMvLy/QJZTnzy6FhYUFBQX57e1gQP6+eMKbTxLV+rRhtVp9+0Ps\ncrk0TTPUL4aqqrqu22z+/sfywOl06rpOlzyjS94w4G+ce48dXfLMvVPTbrcHupBz3F1q0KCB\ncXZZ+b9LVqvVb+8FY/LrX66oqKiCggJd10t/6/Lz8xs2bFjdMWXZbLawsDAfFpmXl6dpmm/n\nrCWn06koSmhoaKALOUdRFEVR6JJndMkb+fn5RvuNc7lcsizTJc9cLpfL5TJUSaVdMk6wM2CX\nYHp+vXiiVatWsizv3bvX/bKgoCA7O7tdu3bVHQMAAIDz+TXYRUdHX3HFFa+++ur+/fsPHz48\nd+7ciy66qH379kKItWvXrlmzxvMYAAAAeODv+9hNnjy5WbNmM2fOnDp1qsPheOyxx9z7zDMy\nMjZt2uR5DAAAADyQ3NeHo5QBT1LmhHdv0CVv0CVvqKqqaZoBLwugS54ZsEsGvMQEpkewAwAA\nMAl/H4oFAABAHSHYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMwkD3KTUC\n90PMMjMzV69eHehaDOrUqVOLFy/+5ZdfXC5XixYtbr/99tatWwe6KMPJzs5esmTJrl27dF1P\nSUm57bbb2rZtG+iijGvdunXz589/9NFHu3fvHuhaDGfy5MlZWVmlL4OCgt59993AlWNcn376\n6QcffHDy5MnGjRuPGTPmsssuC3RFQGAQ7M7ZsGHDokWLOnfunJmZGehajOupp55yOBxPPPFE\ncHDw22+/PWvWrEWLFhnqVu8BpyjK9OnTU1NTn3vuOYvFsmLFiieeeGLx4sXBwcGBLs2I8vLy\nlixZ4nA4Al2IQRUVFU2YMKE08losHGapwLp161asWHHvvfc2bdp048aNb7zxxsUXXxwSEhLo\nuoAA4G/EObIsz5kzh30GHhQWFsbFxd19990tWrRITEwcM2ZMQUFBdnZ2oOsyluLi4sGDB0+a\nNKlx48aJiYk333xzcXHx0aNHA12XQS1YsKBPnz78H1yZwsLChISE2P+Jjo4OdEVGtGLFirFj\nx3bt2jU+Pn7w4MELFy7kJwoXLPbYndO3b18hxN69ewNdiHGFh4dPmzat9OXJkyctFktsbGwA\nSzKgyMjIoUOHur8uLCz86KOPkpOTmzRpEtiqjGnjxo179+69//77169fH+hajEiWZafTuXHj\nxrfeequwsLBly5Zjxoxp3LhxoOsylpMnTx47dkwIMXny5KNHjzZr1uzOO+/k5AdcsNhjhxoq\nLCx8+eWXhwwZ0rBhw0DXYkSapg0fPnz06NHZ2dlPPvkkTwE/X1FR0YIFC+6++24O5VfmzJkz\nUVFRiqKkpaVNnTrV5XJNmzatuLg40HUZy8mTJ4UQX3311ZQpUxYvXtymTZsnnngiPz8/0HUB\ngUGwQ00cOnTooYce6tChw9ixYwNdi0FZLJb58+c//fTTERERjz76aFFRUaArMpz//Oc/Xbp0\n6dSpU6ALMa7IyMilS5c+8MADrVu3bt269ZQpU0pKSn788cdA12VEI0aMSE5ODg8Pv+OOOyRJ\n2rx5c6ArAgKDYIdq++WXX6ZOnTpw4MC77rpLkqRAl2NcycnJl1xyyZQpU/Lz87/99ttAl2Ms\nGRkZW7duveOOOwJdSH0SHBwcFxeXm5sb6EKMxX3eYWhoqPul1WqNjo4+ffp0QIsCAoZgh+rZ\nuXPns88++89//vOmm24KdC0GtW3btgkTJjidTvdLSZJsNk5mLW/t2rXFxcWTJk0aPXr06NGj\n8/Pz586dO3v27EDXZSwHDhx45ZVXFEVxvywpKTlx4kRCQkJgqzKa6Ojohg0b/v777+6XLpfr\nxIkTjRo1CmxVQKDw/805p0+fVlW1sLBQCOH+TBwWFsbZP2W5XK558+YNGjSoWbNmpbsN6FI5\nrVq1KikpmTdv3qhRo+x2+5o1a0pKSi699NJA12UskyZNuv3220tfPvDAA2PGjLn88ssDWJIB\nRUdHb9y4UVGUW265RVXVpUuXhoWF9ejRI9B1GYvFYhk4cOA777yTnJycnJy8fPnyoKAg7mOH\nC5ak63qgazCKO++8Mycnp9ySQYMGBaoeA/rll1+mT59ebuHEiRMHDBgQkHoM68CBA2+++ebO\nnTslSWratOmtt96ampoa6KIMbcyYMWlpadxs6Hz79u1788039+zZY7fb27RpM378ePZFnU/T\ntLfeeuurr74qKipq06ZNWloa16HjgkWwAwAAMAnOsQMAADAJgh0AAIBJEOwAAABMgmAHAABg\nEgQ7AAAAkyDYAQAAmATBDgAAwCQIdkD9M3PmTOmvIiIirrrqqlWrVtXF21155ZVt27b1UMlP\nP/1UF+/rvWuuuaZ58+aBrQEAjIBHigH11bRp01q0aCGE0DQtOzt76dKlw4cPnzdv3n333Vfl\nthkZGZ07d66/9yev7/UDQB0h2AH11aBBg8o+g2vKlCmXXHLJ9OnTJ06cWOXTezds2FDH1dWt\n+l4/ANQRDsUCJhEeHj58+PDCwsLt27e7l3z77bf9+/ePiIgICQnp0qXL4sWL3cuvv/76yZMn\nCyEkSeratat74TvvvNOtW7eQkJCIiIiuXbu+8847PqmqshqEEL179+7Vq9e2bdv69esXERER\nHx8/cuTI0uc1a5o2c+bMJk2aBAUFXXrppWvXrr333nsdDkdl9dtstv37999www3h4eHh4eEj\nRow4deqUT74FAKhHCHaAeYSEhAghZFkWQqxbt65fv34ul+vtt9/+8MMPL7/88nHjxr3wwgtC\niJdffnnw4MFCiPT09GXLlgkhVqxYMXLkyOTk5Pfee2/58uVxcXEjR4785JNPalmPhxqEEA6H\n48CBAxMnTpw2bVpmZuZrr7323nvvTZkyxb32X//61xNPPNGjR4+PPvooLS1t7NixmzZtcge7\n8+sXQqiqOnTo0N69e7/11luTJk167733HnzwwVrWDwD1jw6gvpkxY4YQYuPGjeWWX3nllTab\nLS8vT9f1zp07t2zZsri4uHTtoEGDwsPDz549q+v6uHHjyv76P/PMM3379nU6ne6X+fn5Nptt\n9OjR7pc9e/Zs06ZNtSpx81xDv379hBDff/996dp+/folJSXpuq5pWqNGjTp06KBpmnuV+/qM\n0NBQ98ty9bunWrVqVemSHj16xMfHV1gVAJgYe+yA+urUqVPHjh07duzY0aNH09PTx40b9/33\n348fPz4yMjInJ2fbtm0DBgywWCwl/3PjjTcWFhbu2LHj/KmmTZu2bt069/4wIURERERCQsLB\ngwdrU543NYSEhPTs2bN0k+Tk5GPHjgkhjh07dvz48f79+0uS5F51+eWXd+jQwcPbBQUFDRky\npPRly5Ytc3Nza1M/ANRHXDwB1FcDBgwo+9Jms6Wlpb344otCiCNHjggh5s+fP3/+/HJbHTp0\n6LLLLiu3sKCgYM6cOR988MHBgweLi4uFEKqqNmvWrDbleVNDXFxcuW9B0zQhxPHjx4UQiYmJ\nZde2adNm//79lb1do0aNSlOgEMJut7unAoALCsEOqK/mzp3rvr2cJEmhoaEdOnSIiooqO+CO\nO+4YP358ua1atmx5/lQDBw784Ycfpk6dev3110dFRUmSdN111/mkSO9rKMvpdAohLJa/HFIo\nm9sAABUi2AH1Vffu3cve7qSspk2bCiFUVa1sQFmZmZnffffd+PHjn376afcSRVFOnTqVkpJS\nm/KqVUM50dHR4n/77Urt3r27NvUAwIWAc+wAE4qOju7Wrdvq1avz8vJKFy5duvSxxx5TFEX8\nb++X+2v3VbTJycmlI1977bWSkhJVVeu0Bg9SUlIiIyM/++yz0iXp6ellzw4sWz8AoBR77ABz\neu655/r373/VVVc9+OCDCQkJGzZsePbZZ0ePHm2z2YQQSQtLJH8AAAHHSURBVElJQohnnnnm\n4osvHjRoUJMmTRYuXNipU6eYmJgPPvhgy5Ytffr02bJlyzfffNOtW7ey065atervf//7Sy+9\nlJaWVrrw/fff37x5c9lhHTt27N27t+caPLDZbOPGjXvxxRdvv/32kSNHZmVlzZ49u2fPnhkZ\nGe4BZesfPny4LxoGAKYQ6MtyAVSb55uMlNqwYUP//v3Dw8Ptdnvr1q2fe+45WZbdq7Kzszt3\n7my32933MUlPT7/iiitCQkIaNWo0ceLE/Pz8NWvWxMbGNmzYcPfu3WVvd/Lee+8JIV5++eWy\nlZzv7rvvrrKGfv36NWvWrGzBZW9iUlJScu+998bGxoaGhvbq1evnn38eNWpUWFhYhfV7ngoA\nLhySzsMWAdQH11xzzc6dO90X2wIAKsQ5dgCMaN68ecOHDy89iy4vL2/z5s2dOnUKbFUAYHCc\nYwfAiGJiYlatWjV06NDx48eXlJTMmzevoKCAp4QBgGcEOwBGdNtttwkh5s6dO2rUKF3XO3Xq\n9PHHH7sfHQYAqAzn2AEAAJgE59gBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAA\nAEyCYAcAAGAS/x+zhASnRgtqnwAAAABJRU5ErkJggg=="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_ojCgJGqGDDx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Support Vector Machine (SVM) Classifier**"
      ],
      "metadata": {
        "id": "kQjFHpmsIWGZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "install.packages(\"e1071\")\n",
        "install.packages(\"ggplot2\")\n",
        "library(e1071)\n",
        "library(ggplot2)\n",
        "\n",
        "data <- mtcars\n",
        "data$am <- factor(data$am)\n",
        "\n",
        "features <- data[, c(\"hp\", \"wt\")]\n",
        "\n",
        "svm_model <- svm(am ~ ., data = data.frame(features, am = data$am), kernel = \"linear\", scale = TRUE)\n",
        "\n",
        "\n",
        "pred <- predict(svm_model, features)\n",
        "\n",
        "accuracy <- mean(pred == data$am)\n",
        "print(paste(\"Accuracy:\", round(accuracy * 100, 2), \"%\"))\n",
        "\n",
        "\n",
        "plot_data <- data.frame(features, Actual = data$am, Predicted = pred)\n",
        "ggplot(plot_data, aes(x = hp, y = wt, color = Predicted, shape = Actual)) +\n",
        "  geom_point(size = 3) +\n",
        "  labs(title = \"SVM Classification on mtcars Dataset\") +\n",
        "  theme_minimal()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "HBsvWXzMGDGg",
        "outputId": "5201ac10-da79-46f2-cd9a-1a58e1363884"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n",
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1] \"Accuracy: 93.75 %\"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "plot without title"
            ],
            "image/png": 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dgBAABIgrADAACQBGEHAAAgCcIOAABAEoQdAACAJAg7AAAASRB2AAAAkiDsAAAA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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bo1kuPn0GDI4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bL7qQktTGDLh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WL_QENxsGDOJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4gwBpONEGDRB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}