Import necessary libraries.

   Load the sample data set.

   Assign the independent(X)and dependent(y) variables.

   Pass the variables to the model.

  Check normality of variable.

  Test the correlation.

   Build the linear model from sklearn library.

import pandas as pd

import scipy

from scipy import stats

#import correlation test method

from sklearn.linear_model import LinearRegression

#Load the sample data

data={‘salary’:[100,200,300,

400,500,400,300,200,100,50],

‘age’:[25,26,25,23,30,29,23,23,25,25],

‘rating’:[4,3.24,2.5,2.25,2,

2.25,2.5,2.75,3.2,4.2],’bonus’:[2500,

1200,900,3000,1800,1400,850,250,750,1000]}

#Measuring descriptive statistics

df=pd.DataFrame(data)

print(“Descriptive statistics\n”,df.describe())

#Correlation

print(“Correlation is\n”)

print(df.corr(method=’pearson’))

#Take independent variable

X = df.iloc[:, :1].values

print(“Value of X is\n:”,X)

#Take dependent variable

y = df.iloc[:, 3].values

print(“Value of y is:\n”,y)

#building linear model

regressor = LinearRegression()

#Fit the variable to the linear model

t=regressor.fit(X, y)

#print the results

print(“Regression intercept is”,regressor.intercept_)

print(“Regression coefficient is”,regressor.coef_)