Near Earth Objects (NEOs) Statistical Analysis Project

Overview

This project explores Near Earth Objects (NEOs), particularly asteroids, to assess their potential hazard to Earth through data-driven statistical analysis. Using publicly available datasets and Python-based tools, we analyze orbital and physical properties of asteroids and apply logistic regression to model their hazard classification.

Objectives

• Data Collection & Cleaning: Acquire NEO data, clean it using Excel Power Query and Python.
• Exploratory Data Analysis (EDA): Analyze variable distributions, correlations, and patterns.
• Statistical Analysis: Examine how size, orbit, and type relate to hazard potential.
• Logistic Regression: Train a model to classify whether an asteroid is potentially hazardous.
• Insights & Conclusion: Summarize findings and their implications for planetary defense.

Getting Started

1. Clone the Repository

   git clone https://github.com/HaiderPhys21/NEO_statAnalysis.git
   
   

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Analysis Explore Jupyter notebooks in the notebooks/ directory, which walk through:

   • Data cleaning
   • EDA
   • Logistic regression modeling

Repository Structure

NEO_statAnalysis/
├── data/             # Input data files (optional or downloadable)
├── notebooks/        # Jupyter notebooks with analysis steps
├── requirements.txt  # Python dependencies
├── LICENSE
└── README.md

Citation

If you use this project in your research, please cite it using the Zenodo DOI (after publishing to Zenodo).

License

This project is licensed under the MIT License.

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Created by Syed Haider Ali