🎬 Movie Recommendation System using Streamlit

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Welcome to the Movie Recommendation System built with Python and Streamlit! This app helps users discover movies similar to their favorites and also explore detailed information about any movie in the dataset. It leverages TMDB data and a content-based similarity model to generate recommendations.

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🚀 Features

• 🔍 Movie Recommendation Tab

  • Enter a movie title and get top 9 similar movies with poster, director, rating, cast, and overview.
  • Watch the Trailer of the movies.

• 🎞️ Movie Info Tab

  • Search for any movie in the dataset and view detailed information including release year, cast, runtime, and a link to TMDB.
  • Watch the Trailer of the movie.

• 🖼️ Posters and Details
  Poster images fetched from TMDB API and styled using Streamlit’s clean layout.

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📂 Project Structure

📁 data/
│   ├── movie\_df\_merged\_all.csv
│   ├── movie\_df\_processed.csv
│   ├── similarity\_vector.npy
│   ├── tmdb\_5000\_credits.csv
│   ├── tmdb\_5000\_movies.csv
│   └── tmdb\_extra\_columns\_12M.csv
📄 movie\_df\_cleaning.ipynb     # Data cleaning and preprocessing
📄 movie\_df\_merging.ipynb      # Dataset merging and feature engineering
📄 movie\_recommender.py        # Main Streamlit app file

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🔗 External Dependencies

• Movie poster paths and links use the TMDB API.

• Dataset includes data from:

  • tmdb_5000_movies.csv
  • tmdb_5000_credits.csv
  • Extra columns from tmdb_extra_columns_12M.csv

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👤 Author

Partho Sarothi Das Aspiring Data Scientist | Passionate about ML & Visualization 📧 Email: partho52@gmail.com

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📝 License

This project is licensed under the MIT License. Feel free to use, modify, and share!

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🌟 Acknowledgements

• Streamlit
• The Movie Database (TMDB)