This Movie Recommender System is a web application that recommends similar movies based on user selection. It uses content based filtering techniques to calculate similarity between movies and provides personalized recommendations.
- Select a movie from a dropdown menu or type the movie name.
- Click the "Show Recommendation" button to display similar movies.
- View the recommended movies along with their posters (if available).
- Explore a variety of movie recommendations based on your selections.
You can try out the Movie Recommender System by visiting the live demo here.
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Clone the repository:
git clone <repository-url>
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Install the required dependencies:
pip install -r requirements.txt3.Obtain an API key from The Movie Database (TMDb) to fetch movie data and posters.
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Update the fetch_poster function in app.py with your TMDb API key: url = "https://api.themoviedb.org/3/movie/{}?api_key=YOUR_API_KEY&language=en-US".format(movie_id)
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Run the application:
streamlit run app.py-
Technologies Used Python Streamlit Pandas Requests Data Source The movie dataset used for this project is available in the movies.pkl and similarity.pkl files.
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License This project is licensed under the MIT License.
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Contributing Contributions are welcome! If you have any suggestions, enhancements, or bug fixes, please create a pull request or open an issue.
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Acknowledgements The Movie Database (TMDb) - for providing the movie data and posters.