{"id":27966351,"url":"https://github.com/caesaredia/credit-default-risk-analysis","last_synced_at":"2025-10-04T08:21:55.628Z","repository":{"id":286969895,"uuid":"963143119","full_name":"caesaredia/credit-default-risk-analysis","owner":"caesaredia","description":"Predicting customer loan default risk using exploratory data analysis and basic statistical modeling.","archived":false,"fork":false,"pushed_at":"2025-04-10T05:45:28.000Z","size":601,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-07T20:18:16.354Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/caesaredia.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,"zenodo":null}},"created_at":"2025-04-09T08:15:46.000Z","updated_at":"2025-04-10T05:45:32.000Z","dependencies_parsed_at":"2025-04-09T09:26:58.661Z","dependency_job_id":"175a4947-29a9-4f71-a7bc-b47b68f76fe4","html_url":"https://github.com/caesaredia/credit-default-risk-analysis","commit_stats":null,"previous_names":["caesaredia/credit-default-risk-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/caesaredia/credit-default-risk-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesaredia%2Fcredit-default-risk-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesaredia%2Fcredit-default-risk-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesaredia%2Fcredit-default-risk-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesaredia%2Fcredit-default-risk-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/caesaredia","download_url":"https://codeload.github.com/caesaredia/credit-default-risk-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesaredia%2Fcredit-default-risk-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278284043,"owners_count":25961422,"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","status":"online","status_checked_at":"2025-10-04T02:00:05.491Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":[],"created_at":"2025-05-07T20:18:15.275Z","updated_at":"2025-10-04T08:21:55.604Z","avatar_url":"https://github.com/caesaredia.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Credit Default Risk Analysis\nThis project analyzes factors that may influence the probability of customer loan default using a real-world dataset from a financial institution. The analysis focuses on identifying trends and relationships across demographic and financial variables.\n\n## Project Objectives 📊\n- Understand which customer attributes contribute to higher or lower loan default risk.\n- Provide insights that can help credit divisions make more informed lending decisions.\n- Apply data cleaning, transformation, and exploratory analysis techniques.\n\n## Key Analysis \u0026 Hypotheses Tested 🔍\n- **Number of Children**: Are customers with more children more likely to default?\n- **Family Status**: How does marital status influence default probability?\n- **Income Level**: Do low-income customers default more often?\n- **Loan Purpose**: Are loans for education or car purchases riskier?\n\n## Tools Used\n- Python (pandas, matplotlib, seaborn)\n- Jupyter Notebook\n\n## Files\n- `credit_analysis.ipynb` — the main notebook containing all analysis steps\n- `credit_scoring_eng.csv` — source data file\n\n## Notes\n- Outliers and missing values were addressed using median imputation and basic filtering.\n- The dataset was cleaned for inconsistencies like casing differences and invalid values (e.g., age = 0, children = -1).\n\n## Author\nNabilla Hafsah Caesaredia\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaesaredia%2Fcredit-default-risk-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcaesaredia%2Fcredit-default-risk-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaesaredia%2Fcredit-default-risk-analysis/lists"}