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abhisekbasu/
predictive-maintenance-model

Best Model: Random Forest
Task: Binary Classification (0=Normal, 1=Faulty)
Dataset: abhisekbasu/predictive-maintenance-engine
Training Records: 15,628
Test Records: 3,907
Primary Metric: Recall (Faulty class)

Name: predictive-maintenance-model
Availability: InStock

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Predictive Maintenance — Engine Failure Detection

Model	Accuracy	Precision	Recall	F1	ROC-AUC
Decision Tree	0.5923	0.6789	0.6703	0.6746	0.5647
Random Forest	0.6473	0.6862	0.8116	0.7437	0.6750
XGBoost	0.6222	0.7052	0.6886	0.6968	0.6563

Original : Engine_RPM, Lub_Oil_Pressure, Fuel_Pressure, Coolant_Pressure, Lub_Oil_Temperature, Coolant_Temperature
Engineered : Pressure_Index, Thermal_Index, Pressure_Temp_Ratio, RPM_Pressure_Ratio, Thermal_Deviation

File	Description
best_engine_model_v1.joblib	Trained Random Forest model
model_summary.json	Complete metrics and parameters
mlruns/mlruns.zip	All MLflow experiment logs

All hyperparameter combinations were tracked using MLflow. Complete experiment logs are available in mlruns/mlruns.zip.