The Movie Recommender System is a content-based recommendation application built using Streamlit. It allows users to enter a movie name and specify the number of recommendations they want. The system then suggests movies based on similarities in genres, director, actors, and tags.
- Simple and user-friendly interface powered by Streamlit
- Content-based recommendation system
- Uses movie metadata such as genres, director, actors, and tags
- Generates recommendations based on similarity scores
- Clone the repository:
git clone https://github.com/yourusername/movie-recommender.git cd movie-recommender - Create and activate a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Install dependencies:
pip install -r requirements.txt
- Run the Streamlit app:
streamlit run app.py
- Enter the movie name and specify the number of recommendations.
- View the recommended movies based on similarity.
- The system calculates movie similarity using Text vectorization and cosine similarity.
- It finds movies with similar genres, directors, actors, and tags.
- Based on the similarity scores, the top matching movies are recommended.
Feel free to fork the repository and submit pull requests to improve the recommender system.
This project is licensed under the MIT License.