Product Suggestion System for E-Commerce Platform Using ML

The goal of this project is to develop and deploy a machine learning-driven product recommendation system that uses sophisticated modeling techniques to provide users with precise and pertinent recommendations. In particular, the system incorporates three main strategies: Content-Based Filtering, which takes user preferences and product features into account; Collaborative Filtering, which examines user-item interactions; and a Hybrid model, which combines the advantages of both strategies to lessen their respective drawbacks and enhance overall predictive performance. This system was developed using data from publicly accessible sources like the UCI Machine Learning Repository and Kaggle, as well as dynamic, real-time user interaction logs gathered from simulated interactions. Product descriptions, browsing history, user ratings, and other behavioral variables are all included in the collection. A thorough data pretreatment step that includes cleaning, normalization, and feature extraction guarantees dependable and high-quality model training. A range of machine learning techniques are evaluated using metrics including precision, recall, F1-score, and mean squared error in order to determine the best model. The resulting system provides real-time, personalized product recommendations based on users' browsing interests and habits. This adaptable framework may be used on a variety of e-commerce platforms that are looking for intelligent recommendation systems, and it improves the buying experience.