🎬 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/

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✨ 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

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🛠 Tech Stack

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

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📸 App Preview

Try the live app to see recommendations in action!

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📂 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

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⚙️ 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

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▶️ 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

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🌐 Deployment

The app is deployed using Streamlit Community Cloud.

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

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🎯 Future Improvements

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

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👨‍💻 Author

Shubham Gharat AI & Data Science Enthusiast

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

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