---
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** | **0.8951** |
| Recall | 1.0000 |
| Precision | 0.6304 |
| F1 Score | 0.7733 |
| Accuracy | 0.6304 |
| ROC-AUC | 0.6443 |

## 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