Import necessary libraries.

  Import LinearRegression()from sklearn.

  Plot scatter diagram to check linearity.

  Assign independent variables(X).

  Assign dependent variable(Y).

  Build the regression model.

  Fit X and Y.

#import libraries

from sklearn import linear_model

import pandas as pd

import matplotlib.pyplot as plt

#read the data set

data=pd.read_csv(‘/home/soft27/soft27

/Sathish/Pythonfiles/Employee.csv’)

#creating data frame

df=pd.DataFrame(data)

print(df)

#plotting the scatter diagram for independent variable 1

plt.scatter(df[‘rating’], df[‘salary’], color=’red’)

plt.title(‘rating vs salary’, fontsize=14)

plt.xlabel(‘rating’, fontsize=14)

plt.ylabel(‘salary’, fontsize=14)

plt.grid(True)

plt.show()

#plotting the scatter diagram for independent variable 2

plt.scatter(df[‘bonus’], df[‘salary’], color=’green’)

plt.title(‘bonus vs salary’, fontsize=14)

plt.xlabel(‘bonus’, fontsize=14)

plt.ylabel(‘salary’, fontsize=14)

plt.grid(True)

plt.show()

#assigning the independent variable

X = df[[‘rating’,’bonus’]]

#assigning the dependent variable

Y = df[‘salary’]

#Build multiple linear regression

regr = linear_model.LinearRegression()

#fit the variables in to the linear model

regr.fit(X, Y)

#print the intercept and regression co-efficient

print(‘Intercept: \n’, regr.intercept_)

print(‘Coefficients: \n’, regr.coef_)