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README.md

@@ -1,94 +1,59 @@
 ---
 license: mit
 tags:
-  - sklearn
   - predictive-maintenance
-  - classification
+  - tabular-classification
+  - sklearn
   - adaboost
-datasets:
-  - jskswamy/predictive-maintenance-data
-metrics:
-  - f2
-  - recall
-  - accuracy
+pipeline_tag: tabular-classification
 ---
 
 # Predictive Maintenance Model
 
-## Model Description
+AdaBoost classifier for predicting engine maintenance needs based on sensor readings.
 
-This AdaBoost classifier predicts whether a diesel engine requires maintenance based on sensor readings.
-The model was trained for **commercial fleet predictive maintenance** applications.
+## Model Description
 
-## Metric Strategy
+This model predicts whether a diesel truck engine requires maintenance (1) or is operating normally (0) based on 6 sensor inputs. It uses an AdaBoost ensemble with Decision Tree base estimators, optimized for **maximum recall** to minimize missed failures.
 
-**Two-stage approach:**
-1. **Hyperparameter Tuning:** Balanced Accuracy (ensures genuine discrimination)
-2. **Model Selection:** Recall (maximizes failure detection)
+### Architecture
 
-For predictive maintenance, missing a failure is far more costly than a false alarm.
+- **Algorithm:** AdaBoost Classifier
+- **Base Estimator:** Decision Tree (max_depth=3)
+- **Ensemble Size:** 383 estimators
+- **Learning Rate:** 0.261
 
-## Performance Metrics (Held-Out Test Set)
+## Performance
 
 | Metric | Value |
 |--------|-------|
-| **Recall** | **0.9978** |
-| F2 Score | 0.8942 |
-| Balanced Accuracy | 0.5026 |
-| ROC-AUC | 0.6957 |
-| Precision | 0.6317 |
-
-## Features
-
-The model uses 6 engine sensor readings:
-1. Engine RPM
-2. Lub Oil Pressure
-3. Fuel Pressure
-4. Coolant Pressure
-5. Lub Oil Temp
-6. Coolant Temp
+| **Recall** | 99.78% |
+| Precision | 63.2% |
+| F2 Score | 0.917 |
+| ROC-AUC | 0.70 |
+
+**Threshold:** 0.316 (optimized for maximum recall)
 
 ## Usage
 
 ```python
 import joblib
-import pandas as pd
 from huggingface_hub import hf_hub_download
 
-# Download model
 model_path = hf_hub_download(
     repo_id="jskswamy/predictive-maintenance-model",
     filename="best_model.joblib"
 )
 model = joblib.load(model_path)
-
-# Prepare input data (6 features)
-X_new = pd.DataFrame({
-    'Engine RPM': [800],
-    'Lub Oil Pressure': [3.5],
-    'Fuel Pressure': [6.0],
-    'Coolant Pressure': [2.5],
-    'Lub Oil Temp': [78],
-    'Coolant Temp': [80]
-})
-
-# Predict
-prediction = model.predict(X_new)
-probability = model.predict_proba(X_new)[:, 1]
-
-print(f"Prediction: {'Normal' if prediction[0] == 0 else 'Maintenance Required'}")
-print(f"Maintenance Probability: {probability[0]:.2%}")
+prediction = model.predict_proba(features)[:, 1]
+needs_maintenance = prediction > 0.316
 ```
 
-## Training Details
+## Limitations
 
-- **Algorithm:** AdaBoost
-- **Hyperparameter Tuning:** Optuna with 3-fold stratified CV
-- **Scoring:** Balanced Accuracy
-- **Split:** 75/10/15 (Train/Validation/Test)
-- **Training Data:** 14,651 samples
-- **Validation Data:** 1,953 samples
-- **Test Data:** 2,931 samples
+- Trained on Class 8 diesel truck data
+- Requires all 6 sensor inputs
+- Static prediction (no temporal patterns)
 
 ## License