E-COMMERCE RECOMMENDATION SYSTEM

Project Algorithm :

K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Long Short-Term Memory (LSTM), K-Means Clustering

Project Overview :

With the rapid growth of e-commerce platforms, recommending relevant products to users has become a vital part of improving user satisfaction and boosting sales. This project develops an intelligent recommendation system that suggests products to users based on their browsing behavior, purchase history, and user preferences using collaborative and content-based filtering techniques. Built using Python, the system employs machine learning models and integrates seamlessly into e-commerce platforms. Optional modules include sentiment analysis from reviews and a hybrid recommendation engine for enhanced accuracy.

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