{"id":20718987,"url":"https://github.com/keshav434/loan-application-data-analysis","last_synced_at":"2026-04-16T02:32:13.754Z","repository":{"id":249295275,"uuid":"830951660","full_name":"keshav434/Loan-Application-Data-Analysis","owner":"keshav434","description":"Data Analysis and visualization project involing bias detection and building predictive models using Python.","archived":false,"fork":false,"pushed_at":"2024-08-18T18:41:15.000Z","size":885,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-25T18:43:17.755Z","etag":null,"topics":["data-analytics","data-visualization","python"],"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/keshav434.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":"2024-07-19T10:29:08.000Z","updated_at":"2024-08-18T18:41:17.000Z","dependencies_parsed_at":"2024-07-19T23:12:17.290Z","dependency_job_id":"6f725420-de25-44c2-8738-9ca6cd1cd4f1","html_url":"https://github.com/keshav434/Loan-Application-Data-Analysis","commit_stats":null,"previous_names":["keshav434/loan-application-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/keshav434/Loan-Application-Data-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keshav434%2FLoan-Application-Data-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keshav434%2FLoan-Application-Data-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keshav434%2FLoan-Application-Data-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keshav434%2FLoan-Application-Data-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/keshav434","download_url":"https://codeload.github.com/keshav434/Loan-Application-Data-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keshav434%2FLoan-Application-Data-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31868494,"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":"online","status_checked_at":"2026-04-16T02:00:06.042Z","response_time":69,"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":["data-analytics","data-visualization","python"],"created_at":"2024-11-17T03:15:34.912Z","updated_at":"2026-04-16T02:32:13.727Z","avatar_url":"https://github.com/keshav434.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Project Info:\nRahuri Finance requires building an AI model to predict the \"Loan Payment Failure\" tendency (a binary classification problem) for a given loan application. Additionally, we need to identify any bias towards specific attributes.\n\nProject Steps:\n\n(a) Preprocessing:\n\nDetail the preprocessing steps, including handling missing values, plotting graphs, data discretization, normalization, and data encoding.\n\n(b) Initial Data Exploration:\n\nUtilize techniques like association rule mining and clustering for initial data exploration to identify potential biases. 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