neo_statanalysis
Conducted statistical analysis of Hazardous Near-Earth Asteroids (NEAs) to identify key parameters contributing to their hazard potential. Developed and implemented a Machine Learning Model to model the impact of different parameters on NEA hazard potential, leading to more accurate predictions of future asteroid impacts.
Science Score: 44.0%
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○Scientific vocabulary similarity
Low similarity (13.6%) to scientific vocabulary
Repository
Conducted statistical analysis of Hazardous Near-Earth Asteroids (NEAs) to identify key parameters contributing to their hazard potential. Developed and implemented a Machine Learning Model to model the impact of different parameters on NEA hazard potential, leading to more accurate predictions of future asteroid impacts.
Basic Info
- Host: GitHub
- Owner: HaiderPhys21
- Language: Jupyter Notebook
- Default Branch: main
- Size: 21.6 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
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
Clone the Repository ```bash git clone https://github.com/HaiderPhys21/NEO_statAnalysis.git
Install Dependencies
bash
pip install -r requirements.txt
- Run the Analysis
Explore Jupyter notebooks in the
notebooks/directory, which walk through:
- Data cleaning
- EDA
- Logistic regression modeling
Repository Structure
text
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.
Created by Syed Haider Ali
Owner
- Login: HaiderPhys21
- Kind: user
- Repositories: 1
- Profile: https://github.com/HaiderPhys21
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this code, please cite it as below."
title: "NEO_statAnalysis: Statistical Inference on Near Earth Object Hazards"
version: "1.0.0"
date-released: 2024-06-08
authors:
- family-names: Ali
given-names: Syed Haider
affiliation: Pakistan Intitute of Engineering and Applied Sciences
repository-code: https://github.com/HaiderPhys21/NEO_statAnalysis
oric-id: https://orcid.org/0009-0006-5647-3757
license: MIT
keywords:
- near-earth objects
- asteroid hazard
- logistic regression
- data analysis
- planetary defense
GitHub Events
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- Push event: 1
Last Year
- Push event: 1