Movie Recommender System (TMDB Dataset)

A content-based movie recommender system built with Python that suggests movies based on similarities between movies using the TMDB dataset.

🚀 Features

• Content-based movie recommendations using cosine similarity
• Built with Python and popular data science libraries
• Uses TMDB 5000 Movies Dataset
• Interactive Jupyter notebook for data exploration
• Simple web interface using Streamlit

📁 Project Structure

.
├── app.py                  # Flask web application
├── movie_list.pkl         # Preprocessed movie data
├── similarity.pkl         # Computed similarity matrix
├── movie_recommender.ipynb # Data analysis notebook
├── data/
│   ├── tmdb_5000_movies.csv
│   └── tmdb_5000_credits.csv
├── .env                   # Environment variables
└── .gitignore            # Git ignore file

🛠️ Installation

1. Clone the repository

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

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # For Linux/Mac
venv\Scripts\activate     # For Windows

3. Install required packages

pip install -r requirements.txt

💻 Usage

1. Run the Flask web application:

python app.py

2. Open the Jupyter notebook for data analysis:

jupyter notebook movie_recommender.ipynb

📊 Data Source

The project uses the TMDB 5000 Movie Dataset from Kaggle, which includes:

• Movie metadata (titles, genres, keywords, etc.)
• Movie credits data (cast and crew information)

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

📧 Contact

Your Name - @yourusername Project Link: https://github.com/yourusername/movie-recommender-system-tmdb-dataset