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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]
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