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README.md

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+---
+license: mit
+tags:
+  - sklearn
+  - predictive-maintenance
+  - classification
+  - adaboost
+datasets:
+  - jskswamy/predictive-maintenance-data
+metrics:
+  - f2
+  - recall
+  - accuracy
+---
+
+# Predictive Maintenance Model
+
+## Model Description
+
+This AdaBoost classifier predicts whether a diesel engine requires maintenance based on sensor readings.
+The model was trained for **commercial fleet predictive maintenance** applications.
+
+## Primary Metric: F2 Score
+
+The model is optimized for **F2 Score** (recall weighted 2x over precision) because in predictive maintenance:
+- Missing a maintenance need (False Negative) leads to costly breakdowns
+- A false alarm (False Positive) only results in an extra inspection
+
+## Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| **F2 Score** | **0.8860** |
+| Recall | 0.9752 |
+| Precision | 0.6485 |
+| F1 Score | 0.7790 |
+| Accuracy | 0.6511 |
+| ROC-AUC | 0.6762 |
+
+## Features
+
+The model uses 6 engine sensor readings:
+1. Engine RPM
+2. Lub Oil Pressure
+3. Fuel Pressure
+4. Coolant Pressure
+5. Lub Oil Temp
+6. Coolant Temp
+
+## Usage
+
+```python
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="jskswamy/predictive-maintenance-model",
+    filename="best_model.joblib"
+)
+model = joblib.load(model_path)
+
+# Prepare input data (6 features)
+X_new = pd.DataFrame({
+    'Engine RPM': [800],
+    'Lub Oil Pressure': [3.5],
+    'Fuel Pressure': [6.0],
+    'Coolant Pressure': [2.5],
+    'Lub Oil Temp': [78],
+    'Coolant Temp': [80]
+})
+
+# Predict
+prediction = model.predict(X_new)
+probability = model.predict_proba(X_new)[:, 1]
+
+print(f"Prediction: {'Normal' if prediction[0] == 0 else 'Maintenance Required'}")
+print(f"Maintenance Probability: {probability[0]:.2%}")
+```
+
+## Training Details
+
+- **Algorithm:** AdaBoost
+- **Hyperparameter Tuning:** GridSearchCV with 5-fold stratified CV
+- **Scoring:** F2 Score (beta=2)
+- **Training Data:** 15,628 samples
+- **Test Data:** 3,907 samples
+
+## License
+
+MIT License