Movie Select - Discover Your Movie Mojo

Welcome to "Movie Select - Discover Your Movie Mojo"! This Streamlit application helps you discover new movies based on your favorite selections. Customize your recommendations by various filters and enjoy a tailored movie-watching experience.

• Check out the Application: Movie Select

Features

• Personalized Recommendations: Select your favorite movie and get personalized recommendations.
• Filter Options: Refine your recommendations by the number of recommendations, IMDB rating, release category, and revenue.
• User-Friendly Interface: A visually appealing and intuitive interface to make your movie discovery journey seamless.

Dataset

This application uses a fresh dataset created by IMDB & TMDB Movie Metadata Big Dataset (over 1M).

Title: IMDB & TMDB Movie Metadata Big Dataset (>1M)

Subtitle: A Comprehensive Dataset Featuring Detailed Metadata of Movies (IMDB, TMDB). Over 1M Rows & 42 Features: Metadata, Ratings, Genres, Cast, Crew, Sentiment Analysis and many more…

Detailed Description:

Overview: This comprehensive dataset was created by me by merging the extensive film data available from both IMDB and TMDB API's and numerous datasets, offering a rich resource for movie enthusiasts, data scientists, and researchers. With over 1 million rows and 42 detailed features, this dataset provides in-depth information about a wide variety of movies, spanning different genres, periods, and production backgrounds.

File Information:

• File Size: ≈ 1GB
• Format: CSV (Comma-Separated Values)

Some recommendations are also made based on tags from the sentiment analysis results.

Installation

1. Clone the Repository:

   git clone https://github.com/Shubham235Chandra/MovieSelect.git
   cd movie-select

2. Install the Dependencies:

   pip install -r requirements.txt

3. Run the Application:

   streamlit run app.py

Usage

1. Start the App: Open your terminal and navigate to the project directory. Run streamlit run app.py to start the application.
2. Select a Movie: Use the dropdown to select your favorite movie.
3. Filter Recommendations: Use the sidebar to apply various filters such as the number of recommendations, IMDB rating, release category, and revenue.
4. View Recommendations: Click the "Show Recommendation" button to see your personalized movie recommendations.

Project Structure

• app.py: The main Streamlit application file.
• notebook/
  • movies_dict.pkl: A pickle file containing the movies dictionary.
  • similarity_tags.pkl: A pickle file containing similarity tags.
  • poster_dict.pkl: A pickle file containing movie posters.
  • similarity_main_tags.pkl: A pickle file containing main similarity tags.
  • data/image.jpg: The background image for the app.

Background Image

The background image is set using a URL in the CSS. If you prefer to use a local image, you can modify the CSS accordingly.

Dependencies

• Streamlit
• Pandas
• Pickle
• PIL (Pillow)
• Base64
• sklearns

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgements

• Streamlit for the awesome framework.
• PostImg for hosting the background image.

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Enjoy discovering your movie mojo with "Movie Select"! If you have any questions or feedback, feel free to reach out.

Happy Movie Watching!