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Implementation of Linear Regression using Least Squares and Gradient Descent in Python

by Ahmad Farhan AlShammari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 9
Year of Publication: 2024
Authors: Ahmad Farhan AlShammari
10.5120/ijca2024923446

Ahmad Farhan AlShammari . Implementation of Linear Regression using Least Squares and Gradient Descent in Python. International Journal of Computer Applications. 186, 9 ( Feb 2024), 52-57. DOI=10.5120/ijca2024923446

@article{ 10.5120/ijca2024923446,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Linear Regression using Least Squares and Gradient Descent in Python },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2024 },
volume = { 186 },
number = { 9 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = { 52-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number9/implementation-of-linear-regression-using-least-squares-and-gradient-descent-in-python/ },
doi = { 10.5120/ijca2024923446 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-29T03:28:39+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Linear Regression using Least Squares and Gradient Descent in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 9
%P 52-57
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to develop a linear regression program using least squares and gradient descent in Python. Linear regression helps to find the line that best fits to the data points. The linear regression model is based on a linear polynomial of slope (m) and intercept (c). Least squares is used to minimize the error between the observed and predicted points. Gradient descent is used to find the optimal solution that provides the minimum value of error function. The basic steps of linear regression using least squares and gradient descent are explained: preparing observed points, initializing slope and intercept, computing predicted points, computing partial derivatives, updating slope and intercept, computing error function, making equation of line, and plotting predicted line. The developed program was tested on an experimental dataset from Kaggle. The program successfully performed the basic steps of linear regression and provided the required results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Linear Regression Least Squares Mean Squared Error Gradient Descent Python Programming

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