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E-Commerce Product Recommendation System using Machine Learning Algorithms

by Ataur Rahman, Mamunur Rashid, Mohd. Sultan Ahammad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 28
Year of Publication: 2024
Authors: Ataur Rahman, Mamunur Rashid, Mohd. Sultan Ahammad
10.5120/ijca2024923795

Ataur Rahman, Mamunur Rashid, Mohd. Sultan Ahammad . E-Commerce Product Recommendation System using Machine Learning Algorithms. International Journal of Computer Applications. 186, 28 ( Jul 2024), 49-53. DOI=10.5120/ijca2024923795

@article{ 10.5120/ijca2024923795,
author = { Ataur Rahman, Mamunur Rashid, Mohd. Sultan Ahammad },
title = { E-Commerce Product Recommendation System using Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2024 },
volume = { 186 },
number = { 28 },
month = { Jul },
year = { 2024 },
issn = { 0975-8887 },
pages = { 49-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number28/e-commerce-product-recommendation-system-using-machine-learning-algorithms/ },
doi = { 10.5120/ijca2024923795 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-26T23:00:21.073124+05:30
%A Ataur Rahman
%A Mamunur Rashid
%A Mohd. Sultan Ahammad
%T E-Commerce Product Recommendation System using Machine Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 28
%P 49-53
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Algorithms are used in e-commerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to transform how ecommerce platforms communicate with their users. We have created a model that can customize product recommendations and offers for each unique customer using cutting-edge machine learning techniques, we used PCA to reduce features and four machine learning algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The outcome is advantageous for both the client and the business. In this research, we will examine the model's development and training in detail and show how well it performs using actual data. Learning from machines can change of ecommerce world.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Random Forest Recommendations System Decision Tree PCA E-commerce

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