A web-based movie recommendation system that uses collaborative filtering and graph-based features to provide personalized movie recommendations.

• User profile visualization
• Favorite movies display
• Personalized movie recommendations
• Interactive web interface
• Graph-based feature extraction
• Collaborative filtering

• Python
• Flask
• Pandas
• NumPy
• Scikit-learn
• NetworkX
• Bootstrap
• Font Awesome

1. Clone the repository:

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

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Run the feature extraction script:

python Features100K.py

5. Start the web application:

python app.py

6. Open your browser and navigate to:

http://localhost:5000

movie-recommendation-system/
├── app.py                 # Flask web application
├── Features100K.py        # Feature extraction script
├── requirements.txt       # Python dependencies
├── templates/            # HTML templates
│   └── index.html        # Main web interface
├── datasets/            # MovieLens dataset
│   └── ml-100k/
└── data100k/            # Generated features

1. Enter a user ID (1-943) in the web interface
2. View the user's profile information
3. See the user's favorite movies
4. Get personalized movie recommendations

This project uses the MovieLens 100K dataset, which includes:

• 943 users
• 1,682 movies
• 100,000 ratings

MIT License

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request