The Movie Recommendation System is a personalized movie recommendation engine that suggests movies to users based on their preferences and past viewing history. Leveraging the power of collaborative filtering and machine learning algorithms, this system aims to provide users with relevant and enjoyable movie recommendations.
- Personalized Recommendations: The system analyzes user preferences and past interactions to generate personalized movie recommendations.
- Collaborative Filtering: Utilizes collaborative filtering techniques to recommend movies based on similar users' preferences.
- Content-Based Filtering: Incorporates content-based filtering to recommend movies based on their attributes and similarities to movies previously liked by the user.
- User-Friendly Interface: The interface is designed to be intuitive and easy to use, allowing users to discover new movies effortlessly.
To run this project locally, follow these steps:
- Clone this repository:
git clone https://github.com/yourusername/Movie-Recommendation-System.git - Install the required dependencies.
- Run the Flask application:
python main.py - Access the application in your web browser at
http://localhost:5000
- Enter the movie you enjoy watching in the box.
- Enjoy watching your recommended movies!
- Flask: Web framework for building the user interface and backend logic.
- Python: Programming language used for developing the recommendation algorithms and backend functionality.
- Scikit-learn: Library used for implementing collaborative filtering and other machine learning algorithms.
- HTML/CSS/JavaScript: Frontend development technologies for designing the user interface and enabling interactivity.
- Voicu Bogdan (@zvoicu000)