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Movie Recommender System project, This web application offers the functionality of suggesting a set of five movies based on the user’s selection.

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Microsoft Engage 2022 Project - Movie Recommender System

Overview

Welcome to the Movie Recommender System, a project developed as part of Microsoft Engage 2022. This system leverages the power of Streamlit to provide users with personalized movie recommendations based on a trained dataset. The dataset used is the TMDB5000 dataset from Kaggle, and the recommendation model is built using collaborative filtering.

Features

  • Interactive Interface: User-friendly Streamlit interface for selecting movies and obtaining recommendations.
  • Movie Information: Displays selected movie details, including title and poster.
  • Top 5 Recommendations: Recommends the top 5 movies similar to the selected one, along with their posters.

How to Use

  1. Ensure you have Python installed on your machine.
  2. Install the required libraries by running: pip install streamlit pandas requests.
  3. Clone the repository to your local machine.
  4. Run the Streamlit app using the command: streamlit run main.py.
  5. Select a movie from the dropdown menu and click the "Recommend" button.
  6. Explore the top 5 recommended movies along with their posters.

Code Structure

  • main.py: Streamlit app script.
  • movies.pkl: Pickled file containing trained movie recommender system.
  • similar.pkl: Pickled file containing similarity scores between movies.

API Integration

The project integrates with The Movie Database (TMDb) API to fetch movie posters based on movie IDs.

Acknowledgments

Feel free to contribute, report issues, or suggest improvements. Happy movie watching! 🎬🍿

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Movie Recommender System project, This web application offers the functionality of suggesting a set of five movies based on the user’s selection.

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