multivariate-linear-regression's Introduction

Implementation of Multivariate Linear Regression

Aim

To write a python program to implement multivariate linear regression and predict the output.

Equipment’s required:

Hardware – PCs
Anaconda – Python 3.7 Installation / Moodle-Code Runner

Algorithm:

Step 1:

Import Pandas module

Step 2:

Import linear_module from sklearn

Step 3:

Declare data frame df to read the csv file

Step 4:

Declare variable X equal to df with two arguments Weight and Volume

Step 5:

Declare variable y equal to df with an argument CO2

Step 6:

Declare a variable regr equal to linear_model.LinearRegression()

Step 7:

declare regr.fit(X,y)

Step 8:

Print regr.coeff_

Step 9:

Print regr.intercept_

Step 10:

Declare variable predictedCO2 equal to regr.predict with two arguments 33300, 1300

Step 11:

Print variable predictedCO2

Program:

"Cars.Csv"
V\import pandas as pd
from sklearn import linear_model
import matplotlib.pyplot as plt

df=pd.read_csv("cars.csv")

X=df[['Weight','Volume']]
y=df['CO2']

regr = linear_model.LinearRegression()
regr.fit(X,y)

#Coefficients and intercepts of Model

print('Coefficients: ', regr.coef_)
print('Intercept:',regr.intercept_)

#predict the c02 emission of a car where the weight is 300kg,and the volume is 1300cm3

predictedCO2= regr.predict([[330,130]])

print('Predicted CO2 for the corresponding weight and volume',predictedCO2)