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# Predictive Maintenance Model – RandomForest
This model predicts **Engine Condition** using multiple engine sensor parameters.
## Model Information
- **Best Model:** RandomForest
- **Algorithm:** Tuned RandomForest Classifier
- **Training Method:** GridSearchCV + Cross Validation
- **Task Type:** Classification
## Evaluation Metrics
| | F1-Score |
|:-----------------|-----------:|
| RandomForest | 0.963115 |
| GradientBoosting | 0.775914 |
| AdaBoost | 0.76212 |
## Dataset
Dataset used is hosted in the Hugging Face dataset hub:
- https://huggingface.co/datasets/MohammedSohail/engine_maintenance_data
## How to Use
```python
from huggingface_hub import hf_hub_download
import joblib
import pandas as pd
model_path = hf_hub_download(
repo_id="MohammedSohail/engine_maintenance_model",
filename="models/best_model.pkl"
)
model = joblib.load(model_path)
# new_data = pd.DataFrame([
# [750, 3.5, 6.0, 2.5, 80.0, 75.0] # Example sensor values
# ], columns=['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp'])
# prediction = model.predict(new_data)
# print(f"Predicted Engine Condition: {prediction[0]} (0=Normal, 1=Faulty)")
```
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