Upload README.md with huggingface_hub

README.md

@@ -6,42 +6,22 @@ tags:
 - predictive-maintenance
 - engine-condition
 - scikit-learn
+license: apache-2.0
 datasets:
 - dhani10/engine-condition-dataset
-metrics:
-- accuracy
-- f1
-- precision
-- recall
-- roc_auc
 ---
 
-# Engine Condition Prediction Model
+# Engine Condition Model
 
-This model predicts engine condition (Normal=0 / Faulty=1) from sensor signals for predictive maintenance.
+This repository hosts a scikit-learn pipeline for predicting engine condition (0=Normal, 1=Faulty) from six sensor readings:
 
-## Model Details
-- **Winning Model**: Random Forest
-- **Training Data**: dhani10/engine-condition-dataset
-- **Input Features**: ['Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp']
-- **Target**: Engine Condition (0=Normal, 1=Faulty)
-- **Training Samples**: 15628
-- **Test Samples**: 3907
-- **Registered**: 2025-11-07T07:50:04.426743+00:00
+- `Engine rpm`
+- `Lub oil pressure`
+- `Fuel pressure`
+- `Coolant pressure`
+- `lub oil temp`
+- `Coolant temp`
 
-## Cross-Validation
-- **Best CV Score**: 0.7724
+**Default artifact path expected by apps:** `best_engine_model.joblib`
 
-## Test Set Performance
-- **Accuracy**: 0.6596
-- **F1-Score**: 0.7684
-- **Precision**: 0.6728
-- **Recall**: 0.8957
-- **ROC-AUC**: 0.6992
-
-## Best Hyperparameters
-```json
-{
-  "classifier__max_depth": 5,
-  "classifier__n_estimators": 100
-}
+> Note: If you see this message and you're just testing the deployment flow, the model may be a small placeholder trained on synthetic data so the app can run end-to-end.