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🎬 Movie Recommender System

A clean, interactive Movie Recommendation Web App built with Python and Streamlit that suggests movies based on content similarity. Simple UI, fast recommendations, and deployed live for anyone to try.

🚀 Live App: https://movie-recommender-system-by-shuubham-gharat.streamlit.app/

✨ Features

  • 🔍 Recommend movies based on similarity
  • 🧠 Uses cosine similarity on movie metadata
  • 🎞 Fetches movie posters using API
  • ⚡ Fast & lightweight Streamlit UI
  • 🌐 Fully deployed and publicly accessible

🛠 Tech Stack

  • Python
  • Streamlit – frontend & deployment
  • Pandas / NumPy – data processing
  • Scikit-learn – similarity computation
  • Pickle – model persistence
  • OMDb / TMDB API – movie posters

📸 App Preview

Try the live app to see recommendations in action!

📂 Project Structure

Movie-Recommender-System/
│── app.py                 # Main Streamlit app
│── movies.pkl             # Movie dataset
│── similarity.pkl         # Similarity matrix
│── requirements.txt       # Dependencies
│── README.md              # Project documentation

⚙️ How It Works

  1. Movie data is vectorized using content-based features
  2. Cosine similarity is calculated between movies
  3. User selects a movie
  4. Top similar movies are recommended instantly

▶️ Run Locally

git clone https://github.com/your-username/Movie-Recommender-System.git
cd Movie-Recommender-System
pip install -r requirements.txt
streamlit run app.py

🌐 Deployment

The app is deployed using Streamlit Community Cloud.

🔗 Live URL: https://movie-recommender-system-by-shuubham-gharat.streamlit.app/

🎯 Future Improvements

  • User-based collaborative filtering
  • Genre & rating filters
  • User login & watchlist
  • Better UI animations

👨‍💻 Author

Shubham Gharat AI & Data Science Enthusiast

🔗 GitHub: https://github.com/shubhamgharats

⭐ If you like this project, don’t forget to star the repo!