https://github.com/faisal-khann/movie-recommendation-system

The Movie Recommender System is a Streamlit-based application designed to help users discover movies they might enjoy. By selecting a movie, the system provides personalized recommendations based on similarity, along with the features of recommendation (Displays a list of suggested movies), movie posters, ratings, trailers and user interface.
https://github.com/faisal-khann/movie-recommendation-system

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The Movie Recommender System is a Streamlit-based application designed to help users discover movies they might enjoy. By selecting a movie, the system provides personalized recommendations based on similarity, along with the features of recommendation (Displays a list of suggested movies), movie posters, ratings, trailers and user interface.

• Host: GitHub
• URL: https://github.com/faisal-khann/movie-recommendation-system
• Owner: Faisal-khann
• License: mit
• Created: 2025-01-13T19:00:56.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-08-06T21:11:22.000Z (2 months ago)
• Last Synced: 2025-08-06T22:24:20.808Z (2 months ago)
• Topics: juypter-notebook, machine-learning, pandas, pandas-dataframe, requests
• Language: Jupyter Notebook
• Homepage:
• Size: 3.86 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Movie-Recommendation-System

## Project Description ⬇️



  The Movie Recommender System is a Streamlit-based application designed to help users discover movies they might enjoy.


  By selecting a movie, the system provides personalized recommendations based on similarity, along with the following features:


  

   * Recommendations: Displays a list of suggested movies.

   * Movie Posters: Shows the posters for a visual preview.

   * Ratings and Overviews: Includes average ratings and a short description of each movie.

   * Trailers: Provides links to YouTube trailers for easy access.

   * User Interface: Features a clean and interactive design, enhancing the user experience.

    



## How it Works⬇️

  1. Select a movie from the dropdown.

  2. Click the "Recommend" button.

  3. View a list of similar movies, along with their:

        * Posters

        * Ratings

        * Overviews

        * Links to YouTube trailers

## Technologies Used⬇️

  1. **Python:** Core programming language.

  2. **Streamlit:** Framework for building the web app.

  3. **TMDb API:** Fetches movie details like posters, ratings, and trailers.

  4. **Machine Learning:** Recommendation logic based on movie similarity.

  5. **Pandas:** For handling movie data.

  6. **Pickle:** For saving preprocessed data (not included in this repository).

## Prerequisites⬇️

 1. Install Python (version 3.7 or later).

 2. Install required Python libraries:

         pip install streamlit pandas requests

## Setup⬇️

 1. Prepare Data:

    Since `movie_dict.pkl` and `similarity.pkl` are not provided, you need to generate them:

       * The `movie_dict.pkl` file should contain movie metadata (e.g., movie IDs, titles, etc.).

       * The `similarity.pkl` file should be a precomputed similarity matrix.

       * Use your dataset and appropriate Python libraries to create these files.

2. Clone the Repository:

   

       git clone https://github.com/your-username/Movie-Recommender-System.git

       cd Movie-Recommender-System

   

4. Add the Required Files:

    Place the generated `movie_dict.pkl` and `similarity.pkl` files in the project directory.

   

5. Run the Application:

   

        streamlit run app.py

## API Integration⬇️

   The app uses the [TMDb API](https://developer.themoviedb.org/reference/intro/getting-started) to fetch movie details. Replace the API key in the code (`api_key`) with your own TMDb API key.

## Project Structure⬇️

    Movie-Recommender-System/

    │

    ├── app.py                # Main Streamlit application

    ├── README.md             # Project documentation

    └── requirements.txt      # Dependencies

## Screenshot

![Output](https://github.com/user-attachments/assets/b48deaee-7c2d-4a1c-81e1-f1db3b5bfb33)

## Contributions ⬇️

Contributions are welcome! Feel free to fork this repository, make improvements, and submit pull requests.


    Together, let's make this recommendation system even more powerful and versatile.


## License ⬇️

This project is licensed under the [MIT License](https://github.com/Faisal-khann/Movie-Recommendation-System?tab=MIT-1-ov-file)

2025 Faisal Khan

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