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README.md

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+# Exoplanet Detection Model
+
+This repository contains an XGBoost machine learning model for detecting exoplanets using NASA Kepler mission data.
+
+## Model Description
+
+- **Model Type**: XGBoost Classifier
+- **Task**: Binary classification (planet vs. false positive)
+- **Dataset**: NASA Kepler Exoplanet Archive
+- **Format**: Joblib serialized model
+
+## Files
+
+- `exoplanet_xgb.joblib`: The trained XGBoost model and feature names
+- `requirements.txt`: Python dependencies needed to use the model
+
+## Usage
+
+### Loading the Model
+
+```python
+import joblib
+import xgboost as xgb
+import numpy as np
+
+# Load the model
+arte = joblib.load("exoplanet_xgb.joblib")
+model = arte["model"]
+features = arte["features"]
+
+# Make predictions
+# Prepare your data with the required features
+X = np.array([...])  # Your feature values in the correct order
+dmat = xgb.DMatrix(X, feature_names=features)
+predictions = model.predict(dmat)
+```
+
+### API Server
+
+This model is also available via a FastAPI server. See the repository for `app.py`.
+
+```bash
+pip install -r requirements.txt
+uvicorn app:app --host 0.0.0.0 --port 8000
+```
+
+Then visit `http://localhost:8000/docs` for interactive API documentation.
+
+## Requirements
+
+- Python 3.8+
+- xgboost
+- numpy
+- pandas
+- joblib
+
+## License
+
+This model uses publicly available NASA Kepler data.
+
+## Citation
+
+Data Source: NASA Exoplanet Archive