/Movies_Recommedation

Primary LanguageJupyter Notebook

VS_moviereccccc

Movie Recommender System

This Movie Recommender System is a web application that recommends similar movies based on user selection. It uses content based filtering techniques to calculate similarity between movies and provides personalized recommendations.

Features

  • Select a movie from a dropdown menu or type the movie name.
  • Click the "Show Recommendation" button to display similar movies.
  • View the recommended movies along with their posters (if available).
  • Explore a variety of movie recommendations based on your selections.

Demo

You can try out the Movie Recommender System by visiting the live demo here.

Installation

  1. Clone the repository:

    git clone <repository-url>

  2. Install the required dependencies:

pip install -r requirements.txt

3.Obtain an API key from The Movie Database (TMDb) to fetch movie data and posters.

  1. Update the fetch_poster function in app.py with your TMDb API key: url = "https://api.themoviedb.org/3/movie/{}?api_key=YOUR_API_KEY&language=en-US".format(movie_id)

  2. Run the application:

streamlit run app.py

  1. Technologies Used Python Streamlit Pandas Requests Data Source The movie dataset used for this project is available in the movies.pkl and similarity.pkl files.

  2. License This project is licensed under the MIT License.

  3. Contributing Contributions are welcome! If you have any suggestions, enhancements, or bug fixes, please create a pull request or open an issue.

  4. Acknowledgements The Movie Database (TMDb) - for providing the movie data and posters.