Create a recommendation engine that suggests movies to users based on their past ratings. You can implement both collaborative filtering (user-user or item-item) and content-based filtering, providing a comprehensive understanding of recommender systems.
What you'll build
This project is a comprehensive journey into building a production-ready Movie Recommender System. You will start by exploring the classic MovieLens dataset and proceed to implement two fundamental types of recommendation engines: Content-Based Filtering and Collaborative Filtering. The project doesn't stop at just building models; you will rigorously evaluate their performance using industry-standard metrics, compare their strengths and weaknesses, and then build a more powerful Hybrid model that combines the best of both worlds. To make this a portfolio-ready project, the scope is expanded beyond pure machine learning. You will learn to wrap your final model in a REST API, allowing other applications to consume its predictions. Furthermore, you will build a simple interactive web interface to demonstrate your system in action, containerize the entire application using Docker for portability, and finally, deploy it to a cloud service, making it a complete, end-to-end machine learning application. This project is designed to simulate a real-world development cycle, providing you with a robust piece of work that showcases skills in data science, model building, and MLOps.
What you'll learn
Roadmap
8 steps · 77 tasks
