{"id":21864136,"url":"https://github.com/coder5omkar/LendingClubCaseStudy","last_synced_at":"2025-07-21T02:31:07.661Z","repository":{"id":263640731,"uuid":"889504971","full_name":"coder5omkar/LendingClubCaseStudy","owner":"coder5omkar","description":"Performed exploratory data analysis (EDA) on the loan dataset from the Lending Club Case Study to identify the key factors influencing loan defaults.","archived":false,"fork":false,"pushed_at":"2024-12-23T13:56:10.000Z","size":9691,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-22T12:14:09.464Z","etag":null,"topics":["bivariate-analysis","data-science","exploratory-data-analysis","matplotlib","numpy","pandas","seaborn","univariate-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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/coder5omkar.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":"2024-11-16T13:56:25.000Z","updated_at":"2024-12-23T13:56:14.000Z","dependencies_parsed_at":"2024-12-23T14:56:33.204Z","dependency_job_id":null,"html_url":"https://github.com/coder5omkar/LendingClubCaseStudy","commit_stats":null,"previous_names":["coder5om/lendingclubcasestudy","coder5omkar/lendingclubcasestudy"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/coder5omkar/LendingClubCaseStudy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2FLendingClubCaseStudy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2FLendingClubCaseStudy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2FLendingClubCaseStudy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2FLendingClubCaseStudy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/coder5omkar","download_url":"https://codeload.github.com/coder5omkar/LendingClubCaseStudy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/coder5omkar%2FLendingClubCaseStudy/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266229456,"owners_count":23896268,"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":["bivariate-analysis","data-science","exploratory-data-analysis","matplotlib","numpy","pandas","seaborn","univariate-analysis"],"created_at":"2024-11-28T04:07:36.163Z","updated_at":"2025-07-21T02:31:07.655Z","avatar_url":"https://github.com/coder5omkar.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp\u003e\n\u003cimg src=\"https://img.shields.io/badge/python-3.12.4-blue?logo=python\u0026logoColor=white\" /\u003e\n\u003cimg src=\"https://img.shields.io/badge/numpy-1.26.4-blue?logo=numpy\u0026logoColor=white\" /\u003e\n\u003cimg src=\"https://img.shields.io/badge/pandas-2.2.2-blue?logo=pandas\u0026logoColor=white\" /\u003e\n\u003cimg src=\"https://img.shields.io/badge/seaborn-0.13.2-blue?logo=seaborn\u0026logoColor=white\" /\u003e\n\u003cimg src=\"https://img.shields.io/badge/matplotlib-3.8.4-blue?logo=matplotlib\u0026logoColor=white\" /\u003e\n\u003c/p\u003e\n\n## \u003cu\u003eLending Club Case Study\u003c/u\u003e\n\n### Table of Contents\n* [General Info](#general-information)\n* [Technologies Used](#technologies-used)\n* [Conclusions](#conclusions)\n* [Acknowledgements](#acknowledgements)\n\n### General Info :\n\n``` \nLending loans to high-risk applicants is a major cause of financial losses, commonly \nreferred to as credit loss. Credit loss occurs when a borrower either refuses to \nrepay or defaults on the loan, resulting in the lender losing the funds owed.\n\nThe primary goal of this analysis is to identify these high-risk loan applicants, \nenabling the company to minimize the number of such loans and, in turn, reduce the \ntotal credit loss. This case study focuses on using exploratory data analysis (EDA) \nto pinpoint the factors that contribute to loan default.\n\nThe analysis aims to uncover the key variables that significantly predict loan default.\nBy understanding these factors, the company can improve its risk management and make\nmore informed decisions regarding its loan portfolio \n\n```\n\u003c!-- You don't have to answer all the questions - just the ones relevant to your project. --\u003e\n\n### Conclusions :\n\n``` \nKey Insights on \"Charged-Off\" Loans with Higher Default Risk\n1.Applicant Type: Renters have a higher likelihood of defaulting.\n2.Loan Purpose: Loans taken for debt consolidation show a greater risk of default.\n3.Verification Status: Applications marked as \"Not Verified\" are more likely to default.\n4.Loan Term: Terms exceeding 36 months are associated with higher default rates.\n5.Funding Range: Funded amounts between $5,000 and $10,000 are at higher risk.\n6.Loan Amount: Loans within the $5,000 to $10,000 range have higher chances of default.\n7.Installment Range: Monthly installments between $145 and $274 show increased default risk.\n8.Debt-to-Income (DTI) Ratio: Ratios between 12% and 18% are linked to higher defaults.\nAdditional Observations\n1.Seasonal Trends: December shows a spike in defaults for disbursed loans.\n2.Economic Impact: Financial crises, such as in 2011, correlate with increased charged-off loans.\n\n\nBased on Bi-Variate Analysis of \"Charged-Off\" Loans, the Following Applicant Categories Show the Highest Default Risk:\nApplicants taking loans for home improvement with an income between $60k - $70k\nApplicants with MORTGAGE home ownership and an income between $60k - $70k\nApplicants with loan amounts in the range of $30k - $35k who are charged an interest rate of 15% - 17.5%\n\n```\n\n\u003c!-- You don't have to answer all the questions - just the ones relevant to your project. --\u003e\n\n\n### Technologies Used :\n- [Python](https://www.python.org/) version: 3.12.4\n- [Numpy](https://numpy.org/) version: 1.26.4\n- [Pandas](https://pandas.pydata.org/) version: 2.2.2\n- [Seaborn](https://seaborn.pydata.org/) version: 0.13.2\n- [Matplotlib](https://matplotlib.org/) version: 3.8.4\n\n\u003c!-- As the libraries versions keep on changing, it is recommended to mention the version of library used in this project --\u003e\n\n### Acknowledgements :\n\n- This project was inspired by UpGrad\n- It's part of UpGrad tutorials on Exploratory Data Analysis (EDA) on the learning platform\n\n\n### Contact :\nCreated by [@in/omkaramale](https://github.com/coder5omkar)- feel free to contact me!\n\nDeveloped as part of the Exloratory Data Analysis Module required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.\n\nThis project is open source and available under the [MIT License](https://github.com/coder5om/LendingClubCaseStudy/blob/main/licence.txt).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder5omkar%2FLendingClubCaseStudy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoder5omkar%2FLendingClubCaseStudy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoder5omkar%2FLendingClubCaseStudy/lists"}