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debasishdas1985/
engine-predictive-maintenance-model

scikit-learn

joblib

tabular-classification

predictive-maintenance

xgboost

mit

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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

python

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