{"id":20461476,"url":"https://github.com/pythonbyte/default-loans-analysis","last_synced_at":"2025-06-27T18:04:06.899Z","repository":{"id":181433496,"uuid":"403967489","full_name":"pythonbyte/default-loans-analysis","owner":"pythonbyte","description":"This repository displays a CRISP-DM approach to understand about how Loans enter on default status.","archived":false,"fork":false,"pushed_at":"2021-09-07T12:59:30.000Z","size":128,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-05T11:35:44.173Z","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":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pythonbyte.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-09-07T12:19:58.000Z","updated_at":"2021-09-07T12:59:33.000Z","dependencies_parsed_at":"2023-07-15T16:06:36.765Z","dependency_job_id":null,"html_url":"https://github.com/pythonbyte/default-loans-analysis","commit_stats":null,"previous_names":["pythonbyte/default-loans-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pythonbyte/default-loans-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonbyte%2Fdefault-loans-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonbyte%2Fdefault-loans-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonbyte%2Fdefault-loans-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonbyte%2Fdefault-loans-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pythonbyte","download_url":"https://codeload.github.com/pythonbyte/default-loans-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pythonbyte%2Fdefault-loans-analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262307399,"owners_count":23291070,"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":[],"created_at":"2024-11-15T12:25:47.622Z","updated_at":"2025-06-27T18:04:06.847Z","avatar_url":"https://github.com/pythonbyte.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n### Table of Contents\n\n1. [Installation](#installation)\n2. [Data](#data)\n3. [Project Motivation](#motivation)\n4. [File Descriptions](#files)\n5. [Results](#results)\n6. [Licensing, Authors, and Acknowledgements](#licensing)\n\n\n## Installation \u003ca name=\"installation\"\u003e\u003c/a\u003e\n\nFor this project, some main data science libraries were used and will be needed for properly running the notebook.\n\nThese are the libraries:\n\n* numpy\n* pandas\n* matplotlib\n* seaborn\n* scikit-learn\n\n\n## Data \u003ca name=\"data\"\u003e\u003c/a\u003e\n\nThe data used for this project can be found here on this publicly available repository of [Kaggle](https://www.kaggle.com/gauravduttakiit/loan-defaulter).\n\n\n## Project Motivation\u003ca name=\"motivation\"\u003e\u003c/a\u003e\n\nThe area of Loans has a really interesting machine learning problem which is predicting who has a defaulted loan based on the customer's characteristics. This motivated me to pursue the answer to the following questions.\n\n* Which type of contract is the most defaulted?\n* How the default behavior is divided across the customer's social context.\n* What are the factors that most relate a customer to default a loan?\n\n\n## File Descriptions \u003ca name=\"files\"\u003e\u003c/a\u003e\n\nThe file distribution on this project is pretty straightforward, the CRISP-DM approach used to analyze the Loan dataset is on the default-loans-eda.ipynb notebook, and the rest of the files are license, readme, gitignore.\n\n\n## Results\u003ca name=\"results\"\u003e\u003c/a\u003e\n\nThe results from this data analysis can be found on this vailable [Post](https://medium.com/@eduardommelgaco/this-data-analysis-will-make-you-rethink-how-loans-are-given-bee93bb8fb87).\n\n\n## Licensing, Authors, Acknowledgements\u003ca name=\"licensing\"\u003e\u003c/a\u003e\n\nIf you want to chat about this analysis or other approaches you can find me on [Twitter](https://twitter.com/python_byte). Feel free to use this code for any reference you need. And a great thanks to Kaggle for having this community that provides great datasets and Udacity for enabling this project to happen.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpythonbyte%2Fdefault-loans-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpythonbyte%2Fdefault-loans-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpythonbyte%2Fdefault-loans-analysis/lists"}