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README.md

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 title: Predictive Maintenance
-emoji: 😻
-colorFrom: indigo
-colorTo: indigo
+emoji: 🔧
+colorFrom: blue
+colorTo: green
 sdk: docker
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Engine Predictive Maintenance
+
+This application predicts whether an engine requires maintenance based on sensor readings.
+
+## Features
+
+- Real-time prediction using a trained AdaBoost model
+- 6 sensor inputs: RPM, Oil Pressure, Fuel Pressure, Coolant Pressure, Oil Temp, Coolant Temp
+- Input validation with range warnings
+- Confidence scores with adjustable threshold display
+
+## Model Information
+
+- **Algorithm**: AdaBoost with Optuna hyperparameter tuning
+- **Test Recall**: 99.78% (optimized for catching all maintenance cases)
+- **Threshold**: 0.3163 (optimized for high recall)
+
+## Usage
+
+1. Enter the current sensor readings from the engine
+2. Click "Predict Maintenance Status"
+3. View the prediction result and confidence score
+
+## Links
+
+- [Model on HuggingFace](https://huggingface.co/jskswamy/predictive-maintenance-model)
+- [Dataset on HuggingFace](https://huggingface.co/datasets/jskswamy/predictive-maintenance-data)
+- [GitHub Repository](https://github.com/jskswamy/AIML-LearningBytes)
+
+## License
+
+MIT License - PGP-AIML Capstone Project