indianakhil/engine-predictive-maintenance-model

Engine Predictive Maintenance Model

Model Description

Binary classifier predicting engine health (Normal vs Faulty) from six sensor readings.

• Model Type: GradientBoostingClassifier
• Task: Binary Classification (0=Normal, 1=Faulty)
• Training Data: indianakhil/engine-predictive-maintenance (19,535 records)
• Best Hyperparameters: {'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 100}

Performance (Test Set — 20% holdout)

Metric	Score
Accuracy	0.6660
Precision	0.6906
Recall	0.8518
F1-Score	0.7628
ROC-AUC	0.7018
CV F1 (5-fold)	0.7647

Input Features

Engine_RPM, Lub_Oil_Pressure, Fuel_Pressure, Coolant_Pressure, Lub_Oil_Temperature, Coolant_Temperature

Usage

from huggingface_hub import hf_hub_download
import joblib, pandas as pd
model = joblib.load(hf_hub_download(
    repo_id='indianakhil/engine-predictive-maintenance-model',
    filename='best_model.pkl'))
pred = model.predict(X)  # 0=Normal, 1=Faulty
prob = model.predict_proba(X)[:, 1]  # Fault probability