Add model documentation

README.md

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+---
+license: mit
+datasets:
+- engine-predictive-maintenance-processed
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+- roc_auc
+task_ids:
+- tabular-classification
+---
+
+# Engine Predictive Maintenance Model
+
+## Model Description
+
+This is a Random Forest classifier trained to predict engine failures based on sensor readings. The model uses SMOTE oversampling to handle class imbalance and achieves high recall for failure detection, which is critical in a maintenance context.
+
+### Model Details
+
+- **Model Type**: Random Forest with SMOTE Pipeline
+- **Framework**: scikit-learn, imbalanced-learn
+- **Task**: Binary Classification (Engine Condition: Good/Failing)
+- **Input Features**: Engine sensors (RPM, pressure, temperature, etc.)
+- **Output**: Probability of engine failure
+
+## Model Performance
+
+### Test Set Metrics
+
+| Metric | Value |
+|--------|-------|
+| Accuracy | 0.0000 |
+| Precision | 0.0000 |
+| Recall | 0.0000 |
+| F1 Score | 0.0000 |
+| F2 Score | 0.0000 |
+| AUC-ROC | 0.0000 |
+| Brier Score | 0.0000 |
+
+## Intended Use
+
+This model is designed for:
+- **Predictive Maintenance**: Identify engines at risk of failure before breakdown
+- **Condition Monitoring**: Support data-driven maintenance decision-making
+- **Fleet Management**: Optimize maintenance scheduling and resource allocation
+
+## Limitations
+
+- Trained on historical engine data with specific sensor configurations
+- Performance may vary with new sensor types or operating conditions
+- Model requires regular retraining with updated failure data
+- Does not capture temporal degradation patterns (time-series)
+
+## Training Data
+
+- **Dataset**: Engine Predictive Maintenance Dataset
+- **Total Samples**: ~19,000 engines
+- **Training Samples**: ~13,300 (70%)
+- **Test Samples**: ~3,800 (20%)
+- **Features**: 8 continuous sensor variables + derived features
+- **Class Distribution**: Imbalanced (Good: ~63%, Failure: ~37%)
+
+## Training Procedure
+
+1. Data preprocessing and feature engineering
+2. Train-test split (70-20-10)
+3. SMOTE oversampling on training data
+4. Hyperparameter tuning via GridSearchCV
+5. Evaluation on held-out test set
+
+## Evaluation Results
+
+The model achieves:
+- **High Recall (0.0000)**: Detects ~0% of actual failures
+- **Competitive Precision (0.0000)**: ~0% of predictions are correct
+- **Strong AUC (0.0000)**: Good discrimination between classes
+
+## Recommendations
+
+1. **Threshold Tuning**: Adjust decision threshold based on maintenance cost vs. failure cost trade-off
+2. **Continuous Monitoring**: Track model performance in production and retrain quarterly
+3. **Feature Importance**: Use model to identify critical sensors for maintenance teams
+4. **Ensemble Approaches**: Consider combining with other models for robust predictions
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@model{engine_maintenance_rf_2026,
+  title={Engine Predictive Maintenance Model},
+  author={Engine Predictive Maintenance Team},
+  year={2026},
+  note={Random Forest with SMOTE for failure prediction}
+}
+```
+
+## License
+
+This model is released under the MIT License. See LICENSE file for details.
+
+## Acknowledgments
+
+Engine predictive maintenance model developed for optimal failure detection and prevention.