Welcome to the Movie Recommender System, a project developed as part of Microsoft Engage 2022. This system leverages the power of Streamlit to provide users with personalized movie recommendations based on a trained dataset. The dataset used is the TMDB5000 dataset from Kaggle, and the recommendation model is built using collaborative filtering.
- Interactive Interface: User-friendly Streamlit interface for selecting movies and obtaining recommendations.
- Movie Information: Displays selected movie details, including title and poster.
- Top 5 Recommendations: Recommends the top 5 movies similar to the selected one, along with their posters.
- Ensure you have Python installed on your machine.
- Install the required libraries by running:
pip install streamlit pandas requests. - Clone the repository to your local machine.
- Run the Streamlit app using the command:
streamlit run main.py. - Select a movie from the dropdown menu and click the "Recommend" button.
- Explore the top 5 recommended movies along with their posters.
main.py: Streamlit app script.movies.pkl: Pickled file containing trained movie recommender system.similar.pkl: Pickled file containing similarity scores between movies.
The project integrates with The Movie Database (TMDb) API to fetch movie posters based on movie IDs.
- Dataset: TMDB5000 dataset from Kaggle.
- API: The Movie Database (TMDb) API
Feel free to contribute, report issues, or suggest improvements. Happy movie watching! 🎬🍿