Intelligent Product Recommendation System for E-Commerce using Machine Learning

E-commerce platforms increasingly leverage machine learning to enhance user experience through personalized product recommendations. While traditional static systems often fail to adapt to changing user behavior, machine learning models can dynamically analyze user interactions, purchase history, and browsing patterns to deliver tailored suggestions. This project implements collaborative filtering, content-based filtering, and hybrid recommendation models to generate accurate and relevant product recommendations. The system is designed to improve customer engagement, boost sales, and enhance overall user satisfaction. The ultimate goal is to develop an intelligent recommendation engine capable of understanding individual user preferences and providing real-time, context-aware product suggestions