{"id":24388899,"url":"https://github.com/huacenxu/predict-loan-status","last_synced_at":"2026-04-25T00:34:30.635Z","repository":{"id":198931017,"uuid":"461285567","full_name":"huacenxu/Predict-Loan-Status","owner":"huacenxu","description":"Using the Cross-Industry Standard Process of Data Mining (CRISP-DM), this project analyzes loan data from Prosper to identify key factors that predict loan status.","archived":false,"fork":false,"pushed_at":"2022-06-01T18:47:19.000Z","size":513,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-31T16:08:40.265Z","etag":null,"topics":["bootcamp-project","data-science","data-visualization","data-w","loan-prediction-analysis"],"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/huacenxu.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}},"created_at":"2022-02-19T18:57:07.000Z","updated_at":"2024-12-29T15:15:36.000Z","dependencies_parsed_at":"2023-11-12T19:45:14.093Z","dependency_job_id":null,"html_url":"https://github.com/huacenxu/Predict-Loan-Status","commit_stats":null,"previous_names":["huacenxu/predict-loan-status"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/huacenxu/Predict-Loan-Status","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huacenxu%2FPredict-Loan-Status","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huacenxu%2FPredict-Loan-Status/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huacenxu%2FPredict-Loan-Status/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huacenxu%2FPredict-Loan-Status/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/huacenxu","download_url":"https://codeload.github.com/huacenxu/Predict-Loan-Status/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/huacenxu%2FPredict-Loan-Status/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32246120,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-24T13:21:15.438Z","status":"ssl_error","status_checked_at":"2026-04-24T13:21:15.005Z","response_time":64,"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":["bootcamp-project","data-science","data-visualization","data-w","loan-prediction-analysis"],"created_at":"2025-01-19T14:58:02.327Z","updated_at":"2026-04-25T00:34:30.619Z","avatar_url":"https://github.com/huacenxu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Predict Loan Status \n\n## Project Motivation \n\nBased on the Cross-Industry Standard Process of Data Mining (CRISP-DM), a loan data from Prosper is used to study key factors that predict loan Status. Specifically, I asked the following three questions:\n\n- How do homeownership and employment status predict Loan amount?\n- How do homeownership and employment status predict borrowers’ APR?\n- How does Loan Status vary by homeownership status and employment status?\n\n## File Description\n\n- A Descriptive Jupyter Notebook\n- A README file\n\n## Installation \n\n- NumPy\n- Pandas\n- Seaborn\n- Matplotlib\n\nNo additional installations beyond the Anaconda distribution of Python and Jupyter notebooks.\n\n## Analysis Results \n\nKey results and findings were listed below. Find more on [Medium](https://medium.com/@brinxu1/predict-loan-status-9784e36a5736)\n- For those with a home, I found that borrows' APR is the lowest for full-time employed. \n- For those without a home, it is one of the highest for those who do not have a home.\n- Putting together, it looks like those who are full-time employed and have a home enjoy the highest loan amount as well we the lowest borrower APR.\n\n## Acknowlegements\n\n- Dataset is provided by [Kaggle](https://www.kaggle.com/yousuf28/prosper-loan), an open-source data community. \n- The analysis is benefited from the Udacity instructor and mentor team's help and support. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuacenxu%2Fpredict-loan-status","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhuacenxu%2Fpredict-loan-status","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhuacenxu%2Fpredict-loan-status/lists"}