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.

# 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

├── 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)

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

• 🧠 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

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

cd models
python meta_learning_model.py

cd models 
python computer_vision_model.py

cd scripts
python download_computer_vision_images.py

• 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.

The experiments/ folder contains extensive research:

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

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

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

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

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