Add model card README.md

README.md

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+
+---
+tags:
+- sklearn
+- random-forest
+- classification
+- predictive-maintenance
+- engine-health
+---
+# Engine Predictive Maintenance Model
+
+This repository hosts a Random Forest Classifier model for predicting engine condition (Normal/Faulty) based on sensor readings.
+
+## Model Description
+
+The model is a `RandomForestClassifier` trained on engine sensor data to identify potential failures. It was selected as the best-performing model due to its high recall score, which is critical for minimizing false negatives in predictive maintenance scenarios.
+
+## Training Details
+
+- **Model Type**: Random Forest Classifier
+- **Objective**: Binary Classification (0: Normal, 1: Faulty)
+- **Training Data**: Preprocessed engine sensor data (features: engine_rpm, lub_oil_pressure, fuel_pressure, coolant_pressure, lub_oil_temp, coolant_temp, pressure_ratio, temp_diff; target: engine_condition).
+- **Key Hyperparameters (tuned)**:
+  - `n_estimators`: 50
+  - `max_depth`: 5
+  - `min_samples_split`: 5
+  - `min_samples_leaf`: 1
+
+## Evaluation Metrics (on Test Set)
+
+- **Accuracy**: 0.6601
+- **Precision**: 0.6652
+- **Recall**: 0.9277 (Prioritized metric)
+- **F1-Score**: 0.7748
+
+## How to Use
+
+To load and use this model for inference:
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+import pandas as pd
+
+model_path = hf_hub_download(repo_id="SharleyK/engine-predictive-maintenance-model", filename="random_forest_model.pkl")
+model = joblib.load(model_path)
+
+# Example inference (replace with your actual data)
+# Make sure your input data has the same features and preprocessing steps as training
+example_data = pd.DataFrame([{
+    'engine_rpm': 700.0, 'lub_oil_pressure': 2.5, 'fuel_pressure': 11.8, 'coolant_pressure': 3.2,
+    'lub_oil_temp': 80.0, 'coolant_temp': 82.0, 'pressure_ratio': 0.78, 'temp_diff': -2.0
+}])
+
+prediction = model.predict(example_data)
+print(f"Predicted Engine Condition: {prediction[0]} (0=Normal, 1=Faulty)")
+```
+
+## License
+
+[Specify license, e.g., MIT, Apache 2.0]