🎬 Raso Movie Recommendation System

📌 Introduction

Raso Movie Recommendation System is a machine-learning-powered application that suggests movies based on user preferences. It leverages collaborative filtering and content-based filtering techniques to provide accurate recommendations.

🚀 Live Demo

Experience the app live at:
🔗 Raso Movie Recommendation

📂 Project Structure

.
├── app.py  # Main application file
├── movie-recommendation-system.ipynb  # Jupyter notebook for model training
├── similarity.pkl  # Precomputed similarity matrix
├── movie_dict.pkl  # Processed movie dictionary
├── movies.pkl  # Movie dataset
├── requirements.txt  # Required dependencies
├── start.sh  # Deployment script
├── tmdb_5000_credits.csv  # Movie credits dataset
├── tmdb_5000_movies.csv  # Movie metadata dataset
├── .gitattributes  # Git LFS tracking
├── .gitignore  # Files to ignore
├── LICENSE  # License information
├── README.md  # Documentation
└── vercel.json  # Deployment configuration

🛠️ Installation & Setup

Clone the Repository

git clone https://github.com/Sourabh-Kumar04/Raso-movie-recommendation.git
cd Raso-movie-recommendation

Install Dependencies

pip install -r requirements.txt

Run Locally

streamlit run app.py

📊 Features

✅ Movie recommendations based on similarity scores
✅ User-friendly Streamlit UI
✅ Uses TF-IDF and cosine similarity for recommendations
✅ Deployed online for easy access

🏗️ Built With

• Python 🐍
• Streamlit 🚀
• Pandas, NumPy 📊
• Scikit-Learn 🤖
• Git LFS (for large file handling)

📜 License

This project is licensed under the MIT License.

🌟 Acknowledgments

• TMDB for movie dataset
• Streamlit for web framework
• Scikit-Learn for ML algorithms

📧 Developed by Sourabh Kumar