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README.md

@@ -1,51 +1,51 @@
----
-language: en
-license: mit
-tags:
-  - predictive-maintenance
-  - binary-classification
-  - engine-health
-  - scikit-learn
-datasets:
-  - indianakhil/engine-predictive-maintenance
-metrics:
-  - f1
-  - accuracy
-  - roc_auc
----
-
-# Engine Predictive Maintenance Model
-
-## Model Description
-Binary classifier predicting engine health (Normal vs Faulty) from six sensor readings.
-
-- **Model Type**: AdaBoostClassifier
-- **Task**: Binary Classification (0=Normal, 1=Faulty)
-- **Training Data**: `indianakhil/engine-predictive-maintenance` (19,535 records)
-- **Best Hyperparameters**: `{'learning_rate': 0.5, 'n_estimators': 50}`
-
-## Performance (Test Set — 20% holdout)
-
-| Metric | Score |
-|---|---|
-| Accuracy | 0.6644 |
-| Precision | 0.6787 |
-| Recall | 0.8883 |
-| **F1-Score** | **0.7695** |
-| ROC-AUC | 0.6960 |
-| CV F1 (5-fold) | 0.7663 |
-
-## Input Features
-Engine_RPM, Lub_Oil_Pressure, Fuel_Pressure, Coolant_Pressure,
-Lub_Oil_Temperature, Coolant_Temperature
-
-## Usage
-```python
-from huggingface_hub import hf_hub_download
-import joblib, pandas as pd
-model = joblib.load(hf_hub_download(
-    repo_id='indianakhil/engine-predictive-maintenance-model',
-    filename='best_model.pkl'))
-pred = model.predict(X)  # 0=Normal, 1=Faulty
-prob = model.predict_proba(X)[:, 1]  # Fault probability
-```
+---
+language: en
+license: mit
+tags:
+  - predictive-maintenance
+  - binary-classification
+  - engine-health
+  - scikit-learn
+datasets:
+  - indianakhil/engine-predictive-maintenance
+metrics:
+  - f1
+  - accuracy
+  - roc_auc
+---
+
+# Engine Predictive Maintenance Model
+
+## Model Description
+Binary classifier predicting engine health (Normal vs Faulty) from six sensor readings.
+
+- **Model Type**: AdaBoostClassifier
+- **Task**: Binary Classification (0=Normal, 1=Faulty)
+- **Training Data**: `indianakhil/engine-predictive-maintenance` (19,535 records)
+- **Best Hyperparameters**: `{'learning_rate': 0.5, 'n_estimators': 50}`
+
+## Performance (Test Set — 20% holdout)
+
+| Metric | Score |
+|---|---|
+| Accuracy | 0.6644 |
+| Precision | 0.6787 |
+| Recall | 0.8883 |
+| **F1-Score** | **0.7695** |
+| ROC-AUC | 0.6960 |
+| CV F1 (5-fold) | 0.7663 |
+
+## Input Features
+Engine_RPM, Lub_Oil_Pressure, Fuel_Pressure, Coolant_Pressure,
+Lub_Oil_Temperature, Coolant_Temperature
+
+## Usage
+```python
+from huggingface_hub import hf_hub_download
+import joblib, pandas as pd
+model = joblib.load(hf_hub_download(
+    repo_id='indianakhil/engine-predictive-maintenance-model',
+    filename='best_model.pkl'))
+pred = model.predict(X)  # 0=Normal, 1=Faulty
+prob = model.predict_proba(X)[:, 1]  # Fault probability
+```