README.md

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

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.

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