---
license: mit
library_name: scikit-learn
tags: [tabular-classification, predictive-maintenance, gradient-boosting, mlops]
model-index:
  - name: engine-maintenance-classifier
    results:
      - task: {type: tabular-classification, name: Engine maintenance classification}
        metrics:
          - {type: recall, value: 0.8624}
          - {type: precision, value: 0.6845}
          - {type: f1, value: 0.7632}
          - {type: roc_auc, value: 0.7017}
          - {type: accuracy, value: 0.6627}
---

# Engine Predictive Maintenance Classifier

## Model purpose
Classifies the current condition of a vehicle engine — Normal or Needs Maintenance — from six
standard on-board sensor readings. Decision-support / triage tool, not a time-to-failure forecast.

## Input features (exact order)
1. Engine_RPM
2. Lub_Oil_Pressure
3. Fuel_Pressure
4. Coolant_Pressure
5. Lub_Oil_Temperature
6. Coolant_Temperature

## Target convention
0 = Normal · 1 = Requires Maintenance (Faulty, positive class), per the Great Learning Capstone
Project 3 problem statement.

## Algorithm and pipeline
scikit-learn Pipeline: IQRCapper -> StandardScaler -> GradientBoostingClassifier.
IQRCapper winsorises each feature at its 1.5×IQR fence, learned on training data only
(leakage-safe); defined in the required preprocessing.py module.

## Training dataset repository
https://huggingface.co/datasets/imanandshah/engine-predictive-maintenance
(engine_data.csv, train.csv, test.csv)

## Train/test split
80/20 stratified split of 19,535 de-duplicated records -> 15,628 train / 3,907 test,
preserving ~63% Faulty / 37% Normal. random_state = 42.

## Best parameters (GridSearchCV)
Six families compared with 5-fold stratified CV ROC-AUC; Gradient Boosting tuned across 24
combinations with 3-fold stratified CV optimising F1.
learning_rate = 0.05 · max_depth = 2 · n_estimators = 120 · subsample = 1.0

## Test metrics (held-out, positive class = Faulty)
Recall 0.8624 · Precision 0.6845 · F1 0.7632 · ROC-AUC 0.7017 · Accuracy 0.6627 ·
Balanced accuracy 0.5922 · Train accuracy 0.6774

## Intended use
Prioritising / triaging engines by maintenance risk (rank order by predicted probability).
Best for deciding inspection order, not a standalone pass/fail gate.

## Limitations
- Judges current condition; does not predict a failure on a specific future date.
- Six sensors overlap heavily between classes -> moderate performance ceiling.
- No engine IDs or timestamps in the source data.
- Not a replacement for physical engineering inspection.

## Required module
preprocessing.py (contains IQRCapper) must be importable to unpickle the pipeline.

## Example: load and predict
```python
import joblib, pandas as pd
from huggingface_hub import hf_hub_download
import preprocessing  # noqa: F401
FEATURES = ["Engine_RPM","Lub_Oil_Pressure","Fuel_Pressure",
            "Coolant_Pressure","Lub_Oil_Temperature","Coolant_Temperature"]
path = hf_hub_download(repo_id="imanandshah/engine-maintenance-classifier",
                       filename="engine_maintenance_pipeline.joblib")
model = joblib.load(path)
sample = pd.DataFrame([[746.0,3.16,6.20,2.17,76.82,78.35]], columns=FEATURES)
print(int(model.predict(sample)[0]), float(model.predict_proba(sample)[:,1][0]))
```

## Model version
1.0.0

## Links
- Streamlit Space: https://huggingface.co/spaces/imanandshah/engine-maintenance-app
- GitHub: https://github.com/imanandshah/engine-predictive-maintenance
- Dataset: https://huggingface.co/datasets/imanandshah/engine-predictive-maintenance