{"id":22298120,"url":"https://github.com/atharva309/asteroid-analysis","last_synced_at":"2025-09-10T20:39:38.979Z","repository":{"id":261521303,"uuid":"884548830","full_name":"Atharva309/Asteroid-Analysis","owner":"Atharva309","description":"performing Asteroid data analysis using postgresql, liquibase, and R","archived":false,"fork":false,"pushed_at":"2024-11-27T01:11:06.000Z","size":1036,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T22:45:12.947Z","etag":null,"topics":["analysis","liquibase","naive-bayes-classifier","postgresql","r"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Atharva309.png","metadata":{"files":{"readme":"README.md","changelog":"changelogs/changelog-complete.xml","contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-07T00:30:05.000Z","updated_at":"2024-11-27T01:11:09.000Z","dependencies_parsed_at":"2024-11-07T01:45:20.190Z","dependency_job_id":"ce3401d1-12f8-4cb8-862e-3e3873a9a2e1","html_url":"https://github.com/Atharva309/Asteroid-Analysis","commit_stats":null,"previous_names":["atharva309/asteroid-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atharva309%2FAsteroid-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atharva309%2FAsteroid-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atharva309%2FAsteroid-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Atharva309%2FAsteroid-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Atharva309","download_url":"https://codeload.github.com/Atharva309/Asteroid-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245556959,"owners_count":20634889,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","liquibase","naive-bayes-classifier","postgresql","r"],"created_at":"2024-12-03T17:59:47.011Z","updated_at":"2025-03-25T22:45:16.594Z","avatar_url":"https://github.com/Atharva309.png","language":"Jupyter Notebook","readme":"# Asteroid Classification Project\n\nThis 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.\n\ndataset: [kaggle](https://www.kaggle.com/datasets/basu369victor/prediction-of-asteroid-diameter)\n\n## Project Overview\n\nThe project uses the Naive Bayes classifier to identify and categorize asteroids based on orbital and physical parameters. Key steps include:\n\n- **Data Cleaning**: Processed in PostgreSQL for consistency and completeness.\n- **Feature Engineering**: Binning key features to simplify classification.\n- **Database Management**: Using Liquibase to track database changes.\n- **Manual Naive Bayes in R**: Implemented in R to classify asteroids as NEO or PHA.\n\n## Dataset\n\nAsteroid data features include:\n- `Moid` (Minimum Orbit Intersection Distance)\n- `a` (Semi-major axis)\n- `e` (Eccentricity)\n- `i` (Inclination)\n- `H` (Absolute magnitude)\n\nThese features are grouped into bins to create categorical data for classification.\n\n## Project Setup\n\n### Requirements\n\n- **PostgreSQL**: For data storage and cleaning.\n- **Liquibase**: For database migrations.\n- **R and RStudio**: For implementing the Naive Bayes classifier.\n\n### Installation\n\n1. **Clone the repository** and navigate to the project directory.\n2. **Database Setup**: Create a PostgreSQL database, then use provided changelogs scripts to set up tables and load data.\n3. **Liquibase Migrations**: Run migrations using Liquibase for version control.\n4. **Run Classifier in R**: Execute the Naive Bayes classification in R to predict NEO and PHA categories.\n\n## Results\n\nClassification results and additional analyses are saved and visualized in R, providing insights into asteroid characteristics.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatharva309%2Fasteroid-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fatharva309%2Fasteroid-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatharva309%2Fasteroid-analysis/lists"}