https://github.com/atharva309/asteroid-analysis

performing Asteroid data analysis using postgresql, liquibase, and R
https://github.com/atharva309/asteroid-analysis

analysis liquibase naive-bayes-classifier postgresql r

Last synced: about 1 month ago
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performing Asteroid data analysis using postgresql, liquibase, and R

• Host: GitHub
• URL: https://github.com/atharva309/asteroid-analysis
• Owner: Atharva309
• Created: 2024-11-07T00:30:05.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-27T01:11:06.000Z (about 1 month ago)
• Last Synced: 2024-11-27T02:18:56.641Z (about 1 month ago)
• Topics: analysis, liquibase, naive-bayes-classifier, postgresql, r
• Language: Jupyter Notebook
• Homepage:
• Size: 1010 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: changelogs/changelog-complete.xml

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README

        
# Asteroid Classification Project

This project classifies Near-Earth Objects (NEO) and Potentially Hazardous Asteroids (PHA) using a Naive Bayes classifier on a PostgreSQL database. Data cleaning and feature engineering are done in PostgreSQL, with Liquibase for database versioning, and the classifier is implemented manually in R.

dataset: [kaggle](https://www.kaggle.com/datasets/basu369victor/prediction-of-asteroid-diameter)

## Project Overview

The project uses the Naive Bayes classifier to identify and categorize asteroids based on orbital and physical parameters. Key steps include:

- **Data Cleaning**: Processed in PostgreSQL for consistency and completeness.

- **Feature Engineering**: Binning key features to simplify classification.

- **Database Management**: Using Liquibase to track database changes.

- **Manual Naive Bayes in R**: Implemented in R to classify asteroids as NEO or PHA.

## Dataset

Asteroid data features include:

- `Moid` (Minimum Orbit Intersection Distance)

- `a` (Semi-major axis)

- `e` (Eccentricity)

- `i` (Inclination)

- `H` (Absolute magnitude)

These features are grouped into bins to create categorical data for classification.

## Project Setup

### Requirements

- **PostgreSQL**: For data storage and cleaning.

- **Liquibase**: For database migrations.

- **R and RStudio**: For implementing the Naive Bayes classifier.

### Installation

1. **Clone the repository** and navigate to the project directory.

2. **Database Setup**: Create a PostgreSQL database, then use provided changelogs scripts to set up tables and load data.

3. **Liquibase Migrations**: Run migrations using Liquibase for version control.

4. **Run Classifier in R**: Execute the Naive Bayes classification in R to predict NEO and PHA categories.

## Results

Classification results and additional analyses are saved and visualized in R, providing insights into asteroid characteristics.