E-Commerce Product Recommendation System based on ML Algorithms
Md. Zahurul Haque

TL;DR
This paper presents a machine learning-based recommendation system for eCommerce that personalizes product suggestions using PCA and multiple algorithms, achieving high accuracy and improving user engagement.
Contribution
It introduces a novel eCommerce recommendation model utilizing PCA and four ML algorithms, with RF achieving 99.6% accuracy, enhancing personalization and business outcomes.
Findings
Random Forest achieved 99.6% accuracy
The model reduces features with PCA for better performance
The system improves customer engagement and business benefits
Abstract
Algorithms are used in eCommerce 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…
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Taxonomy
TopicsE-commerce and Technology Innovations
MethodsLogistic Regression · Masked autoencoder · Principal Components Analysis
