README.md

---
license: mit
tags:
- predictive-maintenance
- engine-failure-prediction
- adaboost
- classification
library_name: sklearn
---

# Predictive Maintenance Model - Engine Failure Prediction

## Model Description

This model predicts engine failures for automotive predictive maintenance using sensor data.

**Model Type:** AdaBoost
**Task:** Binary Classification (Normal vs Faulty Engine)
**Framework:** scikit-learn / XGBoost

## Model Performance

### Test Set Metrics
- **Accuracy:** 0.6668
- **Precision:** 0.6854
- **Recall:** 0.8713 (Primary metric - minimizes false negatives)
- **F1-Score:** 0.7673
- **ROC-AUC:** 0.6959

## Model Details

### Hyperparameters
```python
{
  "learning_rate": 0.05,
  "n_estimators": 100
}
```

### Training Information
- **Training Samples:** 15,628
- **Test Samples:** 3,907
- **Features:** 17
- **Training Date:** 2026-02-08 16:01:01

## Features

The model uses 17 features including:
- Engine RPM
- Lubricating oil pressure and temperature
- Fuel pressure
- Coolant pressure and temperature
- Engineered features (temperature-pressure ratios, differentials, etc.)

## Usage

```python
import joblib
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="SharleyK/predictive-maintenance-model",
    filename="best_model.pkl"
)

# Load model
model = joblib.load(model_path)

# Download scaler
scaler_path = hf_hub_download(
    repo_id="SharleyK/predictive-maintenance-model",
    filename="scaler.pkl"
)
scaler = joblib.load(scaler_path)

# Make predictions
X_new_scaled = scaler.transform(X_new)
predictions = model.predict(X_new_scaled)
probabilities = model.predict_proba(X_new_scaled)

# Interpret results
# 0 = Normal/Healthy Engine
# 1 = Faulty/Requires Maintenance
```

## Model Selection

This model was selected from 6 candidates:
- Decision Tree
- Bagging Classifier
- Random Forest
- AdaBoost
- Gradient Boosting
- XGBoost

Selection criteria: Highest test recall (to minimize false negatives - missed failures)

## Business Impact

- Reduces unplanned breakdowns by detecting failures early
- Minimizes emergency repair costs
- Optimizes maintenance scheduling
- Improves fleet availability and safety

## Limitations

- Requires all sensor inputs to be available
- Trained on specific engine types (automotive and small engines)
- Performance may degrade if sensor calibration changes
- Requires periodic retraining with new data

## Citation

```
@model{predictive_maintenance_engine_model,
  author = {SharleyK},
  title = {Predictive Maintenance Model - Engine Failure Prediction},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/SharleyK/predictive-maintenance-model}
}
```

## License

MIT License