| ---
|
| license: mit
|
| tags:
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| - tabular-classification
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| - predictive-maintenance
|
| - xgboost
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| library_name: scikit-learn
|
| ---
|
|
|
| # 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,
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| coolant pressure/temp).
|
|
|
| ## Usage
|
|
|
| ```python
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| import joblib, pandas as pd
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| from huggingface_hub import hf_hub_download
|
|
|
| path = hf_hub_download(repo_id="debasishdas1985/engine-predictive-maintenance-model", filename="best_engine_model.joblib")
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| model = joblib.load(path)
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| sample = pd.DataFrame([{
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| "engine_rpm": 700, "lub_oil_pressure": 2.5, "fuel_pressure": 11.8,
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| "coolant_pressure": 3.2, "lub_oil_temp": 84.1, "coolant_temp": 81.6,
|
| }])
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| print(model.predict(sample), model.predict_proba(sample))
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| ```
|
|
|
| ## Best hyperparameters
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| {'colsample_bytree': 1.0, 'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 200, 'subsample': 0.8}
|
|
|
| ## Test metrics
|
| | metric | value |
|
| |---|---|
|
| | accuracy | 0.6330 |
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| | precision | 0.7323 |
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| | recall | 0.6585 |
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| | f1-score | 0.6935 |
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| | ROC-AUC | 0.6792 |
|
|
|
