Engine Predictive Maintenance — XGBoost Classifier
Binary classifier predicting whether an engine requires maintenance (engine_condition = 1)
based on six sensor readings (RPM, lub-oil pressure/temp, fuel pressure,
coolant pressure/temp).
Usage
import joblib, pandas as pd
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="debasishdas1985/engine-predictive-maintenance-model", filename="best_engine_model.joblib")
model = joblib.load(path)
sample = pd.DataFrame([{
"engine_rpm": 700, "lub_oil_pressure": 2.5, "fuel_pressure": 11.8,
"coolant_pressure": 3.2, "lub_oil_temp": 84.1, "coolant_temp": 81.6,
}])
print(model.predict(sample), model.predict_proba(sample))
Best hyperparameters
{'colsample_bytree': 1.0, 'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 200, 'subsample': 0.8}
Test metrics
| metric | value |
|---|---|
| accuracy | 0.6330 |
| precision | 0.7323 |
| recall | 0.6585 |
| f1-score | 0.6935 |
| ROC-AUC | 0.6792 |