#5, First Floor, 4th Street , Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

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.

   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.

Sapmle Code

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_)

Screenshots