Recommendation System

This project implements a hybrid recommendation engine combining collaborative filtering and content-based filtering. It uses a Python-based machine learning pipeline to analyze user behavior patterns and product metadata. The inference model is served via a REST API, containerized with Docker to ensure consistent performance across environments. Ideally suited for e-commerce platforms looking to boost conversion rates.

• Built hybrid filtering algorithms for personalized suggestions.
• Containerized the inference engine using Docker for consistent deployment.