Movie Recommender System

Welcome to the Movie Recommender System! This project uses Streamlit to provide a simple and interactive way to recommend movies based on similarity of the content. Select a movie, and the system suggests five similar ones along with their posters.

Features

• Interactive Interface: User-friendly interface built with Streamlit.
• Movie Recommendations: Suggests five movies similar to the selected movie.
• Poster Display: Displays movie posters using The Movie Database (TMDb) API.

How It Works

1. Load Data: The application loads movie data and a similarity matrix from precomputed pickle files.
2. User Selection: Users select a movie from a dropdown menu.
3. Recommendation Process:
   • Finds the most similar movies based on the similarity matrix.
   • Fetches movie posters using the TMDb API.
4. Display: Shows recommended movies and their posters in a grid layout.

Project Structure

• app.py: Main application script.
• movies.pkl: Pickle file containing movie data.
• similarity.pkl: Pickle file containing similarity matrix.
• Data Preprocessing.ipynb: Jupyter Notebook containing all the preprocessing.

Data

The movie metadata used in this project is sourced from Kaggle TMDb Movie Metadata. This dataset contains information on movies such as title, movie ID, and other metadata necessary for building the recommendation system.

Skills and Technologies Used

• Python: The core language used for the project.
• Streamlit: For building the web application interface.
• Pickle: For loading and saving data files.
• Requests: For making HTTP requests to the TMDb API.
• Data Processing: Handling and processing data from pickle files.
• Machine Learning: Utilized similarity matrices for recommendations.
• APIs: Integration with The Movie Database (TMDb) API for fetching movie posters.

Acknowledgements

• The Movie Database (TMDb) for the movie data and posters.
• Streamlit for the easy-to-use app framework.
• Kaggle TMDb Movie Metadata for the movie dataset.