🏆 AmazonML Price Prediction Project

An advanced machine learning project for predicting product prices using ensemble methods, meta-learning, and computer vision techniques. Achieved 50.12% SMAPE using sophisticated stacking approaches.

🚀 Quick Start

# Clone the repository
git clone <repository-url>
cd amazonml-price-prediction

# Install dependencies
pip install -r requirements.txt

# Run the best model (Meta-Learning)
cd models
python meta_learning_model.py

📁 Project Structure

├── models/                          # 🎯 Main optimized models (BEST)
│   ├── meta_learning_model.py       # 🥇 Best: 50.12% SMAPE
│   ├── neural_enhanced_model.py     # 🥈 50.45% SMAPE  
│   └── computer_vision_model.py     # 🥉 ~50% SMAPE
│
├── experiments/                     # 🧪 Research & development
│   ├── ensemble/                    # Ensemble approaches
│   ├── optimization/                # Advanced optimizations
│   └── legacy/                      # Earlier experiments
│
├── results/                         # 📊 Model outputs & analysis
├── scripts/                         # 🔧 Utility scripts
├── src/                            # 📚 Core utilities
├── dataset/                        # 💾 Training/test data
└── image_cache/                    # 🖼️ Downloaded images (2,183 files)

🏆 Model Performance

Model	SMAPE	Status	Features
Meta-Learning	50.12%	✅ Best	Advanced stacking + comprehensive features
Neural Enhanced	50.45%	🥈 Second	Deep learning + feature interactions
Computer Vision	~50%	🧪 Experimental	Image features + text analysis
Ensemble Models	70-80%	❌ Need work	Various ensemble attempts

🎯 Target: <48% SMAPE | 📈 Best Achievement: 50.12% SMAPE

🔧 Key Features

• 🧠 Meta-Learning: Advanced stacking with 7 base models
• 🖼️ Computer Vision: Real image feature extraction (2,183+ images)
• 📝 NLP: Comprehensive text feature engineering
• ⚡ Ensemble Methods: Multiple ensemble approaches tested
• 📊 Robust Validation: Cross-validation with proper SMAPE optimization

📋 Requirements

• Python 3.8+
• pandas, numpy, scikit-learn
• lightgbm, xgboost
• PIL (for image processing)
• See requirements.txt for full list

🚀 Usage Examples

Run Best Model

cd models
python meta_learning_model.py

Run Computer Vision Model

cd models  
python computer_vision_model.py

Resume Image Downloads

cd scripts
python download_computer_vision_images.py

📊 Results & Analysis

• Training Data: 75,000 samples
• Test Data: 75,000 samples
• Image Dataset: 140,587 unique URLs (2,183+ downloaded)
• Feature Engineering: 600+ features per model
• Cross-Validation: 5-fold stratified

See results/ folder for detailed performance analysis.

🧪 Experimental Work

The experiments/ folder contains extensive research:

• Ensemble Methods: Gradient boosting combinations
• Optimization: Advanced hyperparameter tuning
• Legacy Models: Early development iterations

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Advanced machine learning techniques
• Meta-learning and stacking approaches
• Computer vision for e-commerce
• Ensemble method research

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