ECOMMERCE FOR PRODUCT RECOMMENDATION USING MACHINE LEARNING

Abstract

The rapid growth of e-commerce platforms has significantly transformed the way consumers purchase products online. With millions of products available across various categories, customers often face difficulties in finding relevant items that match their preferences and interests. Traditional search methods may not always provide personalized results, leading to reduced customer satisfaction and lower sales conversion rates. To address these challenges, intelligent product recommendation systems have become an essential component of modern e-commerce platforms. These systems help users discover products that are relevant to their needs while enhancing the overall shopping experience. This project, E-Commerce for Product Recommendation Using Machine Learning, proposes an intelligent recommendation system that utilizes machine learning algorithms to provide personalized product suggestions to customers. The system begins by collecting customer and product data, including browsing history, purchase records, ratings, reviews, and product attributes. Data preprocessing techniques such as handling missing values, removing duplicates, and feature transformation are applied to improve data quality and ensure effective model performance.