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Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
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Implementation of Feature Selection using Correlation Matrix 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 58
Year of Publication: 2024
Authors: Ahmad Farhan AlShammari
10.5120/ijca2024924341

Ahmad Farhan AlShammari . Implementation of Feature Selection using Correlation Matrix in Python. International Journal of Computer Applications. 186, 58 ( Dec 2024), 29-34. DOI=10.5120/ijca2024924341

@article{ 10.5120/ijca2024924341,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Feature Selection using Correlation Matrix in Python },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 58 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number58/implementation-of-feature-selection-using-correlation-matrix-in-python/ },
doi = { 10.5120/ijca2024924341 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-27T02:46:14+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Feature Selection using Correlation Matrix in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 58
%P 29-34
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to develop a feature selection program using correlation matrix in Python. Feature selection is used to determine the most important features in data. It helps to reduce the number of features, decrease the complexity of computations, increase the accuracy, and improve the performance of the applied model. Correlation matrix is used to measure the correlation between the input (independent) features and the output (dependent) feature. The input features that are highly correlated with the output feature are identified, filtered, and selected. The basic steps of feature selection using correlation matrix are explained: preparing data (input and output), creating transpose of input data, creating data matrix, computing correlation matrix, plotting correlation matrix, selecting features (adding relevant features and removing redundant features), and printing selected features. The developed program was tested on an experimental dataset. The program successfully performed the basic steps of feature selection using correlation matrix and provided the required results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Feature Selection Filtering Correlation Matrix Correlation Coefficient Features Relevant Redundant Python Programming

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