Movie Recommendation System

This is a Movie Recommendation System built using Streamlit, which recommends movies based on the similarity to a selected movie. The system provides the top 5 recommendations along with their posters.

Features

  • Movie Selection: Select a movie from a dropdown list.
  • Recommendations: Get the top 5 recommended movies based on similarity.
  • Poster Display: View the posters of the recommended movies.

Installation

  1. Clone the Repository:

    git clone https://github.com/TonyBhaskar/Recommendation-System.git
    cd Recommendation-system
  2. Install Dependencies: Ensure you have Python installed, then install the required packages:

    pip install streamlit pandas requests
  3. Download Required Files:

    • Ensure you have the movies.pkl and similarity.pkl.gz files in the root directory.

Usage

  1. Run the Application:

    streamlit run app.py
  2. Select a Movie:

    • Use the dropdown to select a movie.
  3. Get Recommendations:

    • Click the "Recommend" button to see the top 5 movie recommendations along with their posters.

File Structure

  • app.py: Main application script for the Streamlit app.
  • movies.pkl: Pickle file containing the list of movies.
  • similarity.pkl.gz: Gzipped pickle file containing the similarity matrix.
  • Main.ipynb: Jupyter notebook file, presumably used for data preprocessing or model building.

Dependencies

  • streamlit: For building the interactive web application.
  • pandas: For handling movie data.
  • requests: For fetching movie posters from the TMDB API.
  • gzip: For decompressing the similarity matrix file.
  • pickle: For loading serialized data.

API

  • The Movie Database (TMDB): Used to fetch movie posters.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

License

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