The Product Recommendation System is a machine learning-based solution designed to enhance user engagement and drive sales by offering personalized product suggestions to customers on an e-commerce platform. This system analyzes user behavior, preferences, purchase history, and product attributes to generate tailored recommendations that help users discover relevant items more efficiently.

The system employs various recommendation techniques, including:

• Collaborative Filtering – suggesting products based on similar users’ interests.

• Content-Based Filtering – recommending items similar to those a user has interacted with.

• Hybrid Models – combining multiple approaches for improved accuracy.

Recommendations are displayed in sections such as "You May Also Like," "Frequently Bought Together," or "Trending Now," significantly improving customer retention and conversion rates.

Key Features:

• Real-time personalized product recommendations

• User behavior and purchase history analysis

• Support for collaborative, content-based, and hybrid filtering

• Scalable integration with existing e-commerce platforms

• Dashboard for monitoring recommendation performance

• Enhanced customer engagement and increased average order value

Technologies Used:

• Languages & Libraries: Python, Scikit-learn, Pandas, NumPy, TensorFlow/Keras (for advanced models)

• Backend: Flask / Django / Node.js

• Database: MySQL / MongoDB / PostgreSQL

• Deployment: AWS / Heroku / Google Cloud Platform