Movie Recommendation System

Welcome to the Movie Recommendation System repository! This project leverages data science and machine learning techniques to provide personalized movie recommendations. Whether you're looking for a casual movie night suggestion or a tailored list based on your unique preferences, this system has you covered.

Features

• Personalized Recommendations: Utilizes collaborative filtering and content-based filtering to suggest movies you'll love.
• Data Processing: Efficiently handles large datasets, including movie metadata and user ratings.
• User-Friendly Interface: Interactive and easy-to-use interface for users to get recommendations quickly.
• Scalability: Designed to handle increasing amounts of data and user interactions.

Project Structure

• data/: Contains the datasets used for training and evaluation.
• notebooks/: Jupyter notebooks for data exploration and model development.
• src/: Source code for the recommendation algorithms and system implementation.
• models/: Pre-trained models and scripts for training new models.
• docs/: Documentation and resources to help you understand and use the system.

Getting Started

1. Clone the Repository:

   git clone https://github.com/jaidh01/Movie-Recommendation-System.git

2. Install Dependencies:

   cd movie-recommendation-system
   pip install -r requirements.txt

3. Run the System:

   python src/main.py

4. Run app.py:

   streamlit run app.py

How It Works

The Movie Recommendation System combines collaborative filtering techniques, such as matrix factorization, with content-based filtering methods to deliver accurate and personalized movie suggestions. The system learns from user ratings and movie metadata to understand preferences and predict future ratings.

Datasets

This project utilizes publicly available datasets TMDB Movies Dataset. Additional data sources can be integrated to enhance recommendations. TMDB Movies Dataset

Contributing

Contributions are welcome! If you have ideas to improve the system or find any issues, feel free to open an issue or submit a pull request.

Contact

For any questions or suggestions, feel free to reach out:

• Email: [email protected]
• LinkedIn: LinkedIn Profile

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Happy recommending! 🎬