Engine Predictive Maintenance - Best Model

Model Summary

Business Objective

• 0 = Normal
• 1 = Faulty

Training Data

• AshishTatapuzha/engine-predictive-maintenance-dataset
• Training file: prepared_data/train.csv
• Testing file: prepared_data/test.csv

Best Model

• Algorithm: Random Forest
• Primary selection metric: Test Recall (minimize missed faulty engines)

Features Used

• Engine rpm
• Lub oil pressure
• Fuel pressure
• Coolant pressure
• lub oil temp
• Coolant temp

Best Hyperparameters

Test Performance

• Recall (primary): 0.6654
• Precision: 0.7317
• F1 Score: 0.6970
• ROC AUC: 0.6700
• Accuracy: 0.6353

Repository Contents

• best_model.joblib : serialized trained model
• feature_columns.json : list of input feature names
• best_model_params.json : best hyperparameters
• best_model_metrics.json : evaluation metrics
• model_comparison.csv : comparison of all tuned models
• all_model_tuning_results.csv : full tuning log across all models
• best_model_classification_report.csv : classification report
• requirements.txt : package versions for reproducibility

Notes

• Decision Tree
• Bagging
• Random Forest
• AdaBoost
• Gradient Boosting
• XGBoost