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

• Dataset: TMDB5000 dataset from Kaggle.
• API: The Movie Database (TMDb) API

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