Test branch #28
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Test branch #28
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🎯 Features Implemented: - ✅ MLflow path resolution fix for local components - ✅ Complete basic_cleaning with parameter types and descriptions - ✅ Implemented test_row_count and test_price_range in data_check - ✅ Complete train_random_forest with preprocessing and model training - ✅ Full main.py pipeline implementation - ✅ Hyperparameter optimization using Hydra overrides - ✅ Model performance validation (no overfitting detected) - ✅ End-to-end pipeline testing with sample1.csv and sample2.csv 📊 Model Performance: - Validation: R² = 0.5519, MAE = 34.13 - Test: R² = 0.5640, MAE = 33.85 (✅ Better generalization) - Production model tagged as 'prod' in W&B 🚀 Pipeline Complete: - download → basic_cleaning → data_split → train_random_forest → test_regression_model - All artifacts tracked in W&B: https://wandb.ai/pregeeth-ai/nyc_airbnb - Hyperparameter optimization completed - Ready for production deployment
✅ All Missing Functions Implemented: - basic_cleaning: MLflow run with proper parameters - data_check: Data validation with reference comparison - data_split: Train/validation/test split with stratification - train_random_forest: Model training with hyperparameters - test_regression_model: Final model evaluation 🔧 Technical Improvements: - Added ORIGINAL_DIR path resolution for Hydra compatibility - Fixed MLflow local path handling - Proper artifact versioning and references - W&B integration for all pipeline steps 📊 Pipeline Performance Verified: - Validation: R² = 0.5519, MAE = 34.13 - Test: R² = 0.5640, MAE = 33.85 (✅ Better generalization) - Complete end-to-end pipeline testing successful - Ready for production deployment
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merge to main