The Movie Recommendation System is designed to suggest 30 related movies to users based on their preferences. The system utilizes both collaborative filtering (user-based and item-based) and content-based filtering to make recommendations. It processes user ratings, analyzes movie metadata, and combines these methods into a hybrid recommendation engine. The code also includes a web interface built with Flask, allowing users to interact with the recommendation system through a web browser.
Libraries Used
- Pandas: Data manipulation and analysis
- NumPy: Numerical computations
- Scikit-Learn: Machine learning algorithms and data processing
- Surprise: Specialized library for building and analyzing recommender systems
- Flask: Web framework to create the web interface
- Matplotlib: Data visualization
- Seaborn: Statistical data visualization