Plotting Correlation matrix using Python
Step 1: Importing the libraries.
Python3
import sklearn import numpy as np import matplotlib.pyplot as plt import pandas as pd |
Step 2: Finding the Correlation between two variables.
Python3
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.
Python3
# 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
Python3
# 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:
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.