Movie Recommender System

Overview

This project implements a movie recommender system using collaborative filtering techniques. The system suggests movies to users based on their viewing history and preferences, as well as the preferences of similar users.

Features

• User-based collaborative filtering
• Item-based collaborative filtering
• Hybrid recommendation approach
• Integration with TMDB API for movie metadata
• Streamlit-based user interface for easy interaction and visualization

Technologies Used

• Python 3.8+
• Pandas for data manipulation
• Scikit-learn for machine learning algorithms
• Streamlit for the web application interface

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/movie-recommender.git
   cd movie-recommender
   

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

Usage

1. Start the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and navigate to the URL provided by Streamlit (typically http://localhost:8501).

3. Use the interactive interface to:

   • Rate movies
   • Get personalized movie recommendations
   • Explore movie details and statistics

Contributing

We welcome contributions to improve the movie recommender system! Please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-branch-name.
5. Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the TMDB API for providing movie data.
• Inspired by various open-source recommender system projects.
• Streamlit for making it easy to create interactive data science applications.