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Asteroid Hazard Classification for Planetary Defense

Research Project: Lightweight Machine Learning for Near-Earth Object Risk Assessment

Project Overview

Binary classifier to predict Potentially Hazardous Asteroids (PHAs) using orbital parameters, achieving planetary defense-grade accuracy while being 100x faster than deep learning approaches.

Dataset

  • Source: NASA Asteroids Classification (Kaggle)
  • Size: 90,836 asteroids
  • Features: Absolute magnitude, diameter, velocity, miss distance, eccentricity
  • Target: Binary (hazardous: 9%, non-hazardous: 91%)

Quick Start

Setup Environment

python3 -m venv asteroid_env source asteroid_env/bin/activate pip install -r requirements.txt cat > requirements.txt << 'EOF'

Core Data Science

numpy==1.24.3 pandas==2.0.3 scipy==1.11.1

Machine Learning

scikit-learn==1.3.0 xgboost==1.7.6 imbalanced-learn==0.11.0

Visualization

matplotlib==3.7.2 seaborn==0.12.2 plotly==5.15.0

Jupyter

jupyter==1.0.0 ipykernel==6.25.0 ipywidgets==8.1.0

Data Download

kaggle==1.5.16

Model Persistence

joblib==1.3.2

Utilities

tqdm==4.66.1 astropy==5.3.2 python-dotenv==1.0.0