{"id":19700141,"url":"https://github.com/dipeshgoyal013/salary-data-analysis","last_synced_at":"2026-04-15T15:31:06.665Z","repository":{"id":251488691,"uuid":"837565073","full_name":"dipeshgoyal013/Salary-Data-Analysis","owner":"dipeshgoyal013","description":"Salary Analysis according department and agency.","archived":false,"fork":false,"pushed_at":"2024-08-03T11:23:27.000Z","size":783,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-25T02:44:16.111Z","etag":null,"topics":["analysis","matplotlib","numpy","pandas","salary","sklearn-library"],"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/dipeshgoyal013.png","metadata":{"files":{"readme":"README.md","changelog":null,"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":"2024-08-03T10:56:16.000Z","updated_at":"2024-08-03T11:27:19.000Z","dependencies_parsed_at":"2024-08-03T12:32:49.248Z","dependency_job_id":"eefe703c-0c77-47b3-a902-1276190e4fa3","html_url":"https://github.com/dipeshgoyal013/Salary-Data-Analysis","commit_stats":null,"previous_names":["dipeshgoyal013/salary-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dipeshgoyal013/Salary-Data-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FSalary-Data-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FSalary-Data-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FSalary-Data-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FSalary-Data-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dipeshgoyal013","download_url":"https://codeload.github.com/dipeshgoyal013/Salary-Data-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dipeshgoyal013%2FSalary-Data-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31847502,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-15T15:24:51.572Z","status":"ssl_error","status_checked_at":"2026-04-15T15:24:39.138Z","response_time":63,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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","matplotlib","numpy","pandas","salary","sklearn-library"],"created_at":"2024-11-11T21:04:02.787Z","updated_at":"2026-04-15T15:31:06.641Z","avatar_url":"https://github.com/dipeshgoyal013.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Salary-Data-Analysis\n\n### Tools Used\nPython Version: 3.7\nPackages: pandas, numpy, sklearn, matplotlib, seaborn\n\n### Featuring Engineering\nCreating New Column using Date Column for Better analysis\n![image](https://github.com/user-attachments/assets/e2c9a198-1a46-4f2b-8370-ecc1c7f326b6)\n\n### EDA\nI looked at the distributions of the data and the value counts for the various categorical variables. Below are a few highlights from the tables.\n![image](https://github.com/user-attachments/assets/3a7e3003-b21b-4f47-bf90-ef04aa610659)\n![image](https://github.com/user-attachments/assets/b1167697-fc25-4c4f-b391-8fc9906405f5)\n\n### Model Building\nFirst, I transformed the categorical variables into dummy variables using encoding technique. I also split the data into train and tests sets with a test size of 25%.\n\nI tried Linear Regression models and evaluated them using Mean Squared Error.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshgoyal013%2Fsalary-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdipeshgoyal013%2Fsalary-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdipeshgoyal013%2Fsalary-data-analysis/lists"}