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##### How to build simple linear regression model using python?
###### Description

To build simple linear regression model for train data in python.

###### Process

Import necessary libraries.

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.

###### Sapmle Code

import pandas as pd

import scipy

from scipy import stats

#import correlation test method

from sklearn.linear_model import LinearRegression

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_)