---
title: Engine Condition Prediction
sdk: docker
---

# Engine Condition Prediction

This Streamlit application predicts the **engine condition (Normal or Faulty)** using an **XGBoost machine learning model**.

## Model Details
- **Algorithm**: XGBoost Classifier
- **Model Source**: Hugging Face Model Hub
- **Input Features**:
  - Engine rpm
  - Lub oil pressure
  - Fuel pressure
  - Coolant pressure
  - lub oil temp
  - Coolant temp

## How It Works
1. User enters real-time engine sensor values.
2. The app loads a pre-trained XGBoost model from Hugging Face.
3. The model predicts the engine condition.
4. Inputs and predictions are stored in a CSV file for logging.

## Deployment
- **Framework**: Streamlit
- **Containerized with**: Docker
- **Hosted on**: Hugging Face Spaces

## Dependencies
All dependencies are defined in `requirements.txt` and installed during Docker build.

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