🎬 Cine_Match — Movie Recommendation System

🚀 A powerful Movie Recommendation System built using Machine Learning techniques, combining both Collaborative Filtering and Content-Based Filtering to deliver personalized movie suggestions.

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🌟 Features

✨ Hybrid Recommendation System

• 🤝 Collaborative Filtering (SVD) — Recommends based on user behavior
• 🎯 Content-Based Filtering (TF-IDF + Cosine Similarity) — Recommends based on movie titles

📊 Model Evaluation

• 📉 RMSE (Root Mean Squared Error) used for performance evaluation

⚡ Efficient & Scalable

• Uses dimensionality reduction (SVD) for faster computation

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🧠 Tech Stack

• 🐍 Python
• 📊 Pandas, NumPy
• 🤖 Scikit-learn
• 🧮 TruncatedSVD
• 📝 TF-IDF Vectorizer
• 📐 Cosine Similarity

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📂 Dataset

📌 This project uses the MovieLens Dataset 🎥

• Contains user ratings and movie metadata
• Widely used for building recommendation systems

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⚙️ Project Structure

Cine_Match/
│── Cine_Match.py
│── u.data
│── u.item
│── README.md

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🚀 How It Works

🔹 1. Data Preprocessing

• Loads user ratings and movie titles
• Merges datasets
• Creates a user-item matrix

🔹 2. Collaborative Filtering

• Applies Truncated SVD
• Reconstructs rating matrix
• Predicts missing ratings

🔹 3. Content-Based Filtering

• Converts movie titles → TF-IDF vectors
• Computes similarity using cosine similarity
• Recommends similar movies

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📊 Model Evaluation

✔ The model is evaluated using:

• RMSE (Root Mean Squared Error)

Lower RMSE ⇒ Better predictions 🎯

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💻 How to Run

🔧 Step 1: Clone the Repository

git clone https://github.com/aeindri-tech/Cine_Match.git
cd Cine_Match

▶️ Step 2: Run the Script

python Cine_Match.py

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🎯 Example Output

👤 Collaborative Filtering

Top Recommendations for User 10
Movie A
Movie B
Movie C

🎬 Content-Based Filtering

Movies similar to: Toy Story (1995)
Movie X
Movie Y
Movie Z

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🔥 Future Improvements

• 🎨 Add a Web UI (Streamlit / Flask)
• 📈 Use advanced models (Neural Collaborative Filtering)
• 🧠 Improve content features (genres, descriptions)
• 🌐 Deploy on cloud

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🤝 Contributing

Contributions are welcome! Feel free to fork this repo and improve it 🚀

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📜 License

This project is licensed under the MIT License

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👩‍💻 Author

Crafted with curiosity, code, and a passion for Machine Learning 🚀

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