| --- |
| 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 |
|
|
