Plotting Correlation Matrix using Python

Correlation means an association, It is a measure of the extent to which two variables are related. 

1. Positive Correlation: When two variables increase together and decrease together. They are positively correlated. '1' is a perfect positive correlation. For example - demand and profit are positively correlated the more the demand for the product, the more profit hence positive correlation.

2. Negative Correlation: When one variable increases and the other variable decreases together and vice-versa. They are negatively correlated. For example, If the distance between magnet increases their attraction decreases, and vice-versa. Hence, a negative correlation. '-1'  is no correlation

3. Zero Correlation( No Correlation): When two variables don't seem to be linked at all. '0' is a perfect negative correlation. For Example, the amount of tea you take and level of intelligence.

Plotting Correlation matrix using Python

Step 1: Importing the libraries.

import sklearn
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Step 2: Finding the Correlation between two variables.

y = pd.Series([1, 2, 3, 4, 3, 5, 4])
x = pd.Series([1, 2, 3, 4, 5, 6, 7])
correlation = y.corr(x)
correlation 

Output:

Step 3: Plotting the graph. Here we are using scatter plots. A scatter plot is a diagram where each value in the data set is represented by a dot. Also, it shows a relationship between two variables.

# plotting the data
plt.scatter(x, y)

# This will fit the best line into the graph
plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))
         (np.unique(x)), color='red')

Output:

Remember the points that were explained above. Observe both the images you will find similarity Also, observe the value of the correlation is near to 1, hence the positive correlation is reflected.

Adding title and labels in the graph

# adds the title
plt.title('Correlation')

# plot the data
plt.scatter(x, y)

# fits the best fitting line to the data
plt.plot(np.unique(x), 
         np.poly1d(np.polyfit(x, y, 1))
         (np.unique(x)), color='red')

# Labelling axes
plt.xlabel('x axis')
plt.ylabel('y axis')

Output:

Plot using Heatmaps

There are many ways you can plot correlation matrices one efficient way is using the heatmap. It is very easy to understand the correlation using heatmaps it tells the correlation of one feature(variable) to every other feature(variable). In other words, A correlation matrix is a tabular data representing the ‘correlations’ between pairs of variables in a given data.

import seaborn as sns

# checking correlation using heatmap
#Loading dataset
flights = sns.load_dataset("flights")

#plotting the heatmap for correlation
ax = sns.heatmap(flights.corr(), annot=True)

Output: