🎥 Movie Recommendation System

This project is a Machine Learning-based Movie Recommendation System developed during my internship at YBI Foundation. The system uses collaborative filtering techniques to recommend movies based on user preferences and behavior. It was implemented on Google Colab using Python and popular machine learning libraries.

📌 Features

• Personalized Recommendations: Suggests movies tailored to user preferences.
• Collaborative Filtering: Utilizes user and item similarities for recommendation.
• Efficient Computation: Optimized for performance with scalable machine learning algorithms.
• Interactive Interface: Simple and intuitive design for user interaction (if applicable).

🔧 Technologies and Tools

• Platform: Google Colab
• Programming Language: Python
• Libraries Used:
  • Pandas: Data manipulation and analysis
  • NumPy: Numerical computing
  • Scikit-learn: Building and evaluating the recommendation model
  • Matplotlib/Seaborn: (If used) Data visualization

🚀 How It Works

1. Dataset: Used a publicly available movie dataset containing user ratings and movie details.
2. Preprocessing: Cleaned and prepared the dataset for training the model.
3. Model: Implemented a collaborative filtering algorithm to predict user preferences.
4. Evaluation: Assessed model performance using metrics like RMSE or precision-recall.

📂 Project Structure

/  
|-- data/                 # Dataset files  
|-- notebook.ipynb        # Main project notebook (Google Colab)  
|-- README.md             # This file  

💡 Learning Outcomes

• Gained hands-on experience in implementing machine learning algorithms.
• Strengthened data preprocessing and model evaluation skills.
• Learned collaborative filtering techniques for recommendation systems.

🤝 Future Scope

• Extend the system to include hybrid filtering methods.
• Add a web interface for better user accessibility.
• Incorporate more features like genre-based filtering and popularity trends.

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Let me know if you need help refining this or adding more details! 😊