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13fcdab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ---
license: mit
tags:
- tabular-classification
- predictive-maintenance
- xgboost
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,
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 |
|