mushtaq96 / PyMovieRec

A Python-based movie recommendation system utilizing precomputed similarity data and the TMDb API. Powered by Streamlit, Pandas, and machine learning techniques.

Movie Recommendation System

Overview

This project implements a movie recommendation system using precomputed similarity data and the TMDb API. The system generates personalized movie recommendations based on a user-selected movie. It is implemented in Python, utilizing Pandas for data manipulation and Streamlit for the web application.

Data

The recommendation system relies on two precomputed data files:

• movie_list.pkl: Contains information about movies, including title, genres, keywords, cast, and crew.
• similarity.pkl: Stores precomputed similarity scores between movies.

Implementation

Data Preprocessing

The movie_recommendation.ipynb notebook contains code for data preprocessing. It loads data from tmdb_5000_credits.csv and tmdb_5000_movies.csv, merges them, cleans and transforms the data, and generates the movie_list.pkl and similarity.pkl files.

User Interface

The app.py file contains code for the Streamlit app providing the user interface. Users can select a movie from a dropdown list or type in a moive name and receive recommendations, along with movie posters fetched from the TMDb API.

Usage

To run the Streamlit app locally:

1. Install dependencies: pip install -r requirements.txt
2. Set up your TMDb API key by creating a .env file in the root directory with the following content: TMDB_API_KEY=your_api_key_here
3. Run the app: streamlit run app.py

Example Usage

Titanic Recommendation

In this screenshot, the user has entered "Titanic" as the selected movie. The application has responded with a list of recommended movies tailored to the user's input.

Jurassic World Recommendation

This screenshot demonstrates the movie recommendation system in action. The user has selected "Jurassic World" from the dropdown menu, and the application has provided a list of recommended movies based on this selection.

Future Enhancements

• Incorporate machine learning techniques for recommendation, such as collaborative or content-based filtering.
• Explore the use of deep learning to improve recommendation quality.
• Enhance user interface with more interactive features and better visualization.

Contributing

Contributions to this project are welcome! If you have suggestions for improvements or find any issues, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.