The Movie Recommender System is a Python-based project designed to help users discover new movies based on their preferences. By leveraging popular recommendation algorithms like Content-Based Filtering and Collaborative Filtering, the system suggests movies similar to those the user has previously watched or rated. The system also provides recommendations based on specific genres, allowing users to find movies that suit their tastes.
The project uses data from the TMDB Movie Metadata Dataset and employs various machine learning techniques to analyze movie features and user preferences. The result is a personalized movie recommendation list that helps users find new content to watch based on their individual preferences and interests.
- Content-Based Filtering: Recommends movies based on the similarity of movie attributes like genre, director, and cast.
- Collaborative Filtering: Provides recommendations by analyzing user interactions and ratings to suggest movies that other similar users have liked.
- Genre-Based Recommendations: Suggests movies from the same genre as the user’s input.
- Interactive Interface: Users can input their preferences to receive movie recommendations, which can be extended with a web interface in the future.
This system can be easily customized to include more features, data sources, and even be deployed into an interactive web application.
The following libraries were used in the development of this project:
- numpy: For numerical computations and handling large datasets.
- pandas: For data manipulation and analysis.
- scikit-learn: For implementing machine learning algorithms, such as collaborative filtering and content-based recommendation models.
- matplotlib: For visualizations (optional, if needed).
- streamlit: For creating an interactive web interface (optional).
These libraries are the core dependencies required for building the movie recommender system and running the necessary algorithms.