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README.md

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+---
+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 |