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If so, what should the repayment terms be in business recommendations?","archived":false,"fork":false,"pushed_at":"2024-06-28T11:50:44.000Z","size":2399,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-10T16:18:10.334Z","etag":null,"topics":["confusion-matrix","encoding","feature-engineering","featureimportance","hyperparameter-tuning","logistic-regression","machine-learning","numpy","pandas-python","precision-recall","roc-auc-curve","sklearn-metrics","smote-sampling"],"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/Niteshchawla.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":"2024-06-28T11:47:18.000Z","updated_at":"2024-07-16T17:27:21.000Z","dependencies_parsed_at":"2024-06-28T13:06:58.931Z","dependency_job_id":"a0d280ed-2f64-4728-9301-d7dca6833850","html_url":"https://github.com/Niteshchawla/LoanTap-LogisticRegression","commit_stats":null,"previous_names":["niteshchawla/loantap-logisticregression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Niteshchawla%2FLoanTap-LogisticRegression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Niteshchawla%2FLoanTap-LogisticRegression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Niteshchawla%2FLoanTap-LogisticRegression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Niteshchawla%2FLoanTap-LogisticRegression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Niteshchawla","download_url":"https://codeload.github.com/Niteshchawla/LoanTap-LogisticRegression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241044063,"owners_count":19899468,"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":["confusion-matrix","encoding","feature-engineering","featureimportance","hyperparameter-tuning","logistic-regression","machine-learning","numpy","pandas-python","precision-recall","roc-auc-curve","sklearn-metrics","smote-sampling"],"created_at":"2024-11-11T23:13:40.978Z","updated_at":"2026-05-10T12:38:23.961Z","avatar_url":"https://github.com/Niteshchawla.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# LoanTap-LogisticRegression\nContext:\n\nLoanTap is an online platform committed to delivering customized loan products to millennials. They innovate in an otherwise dull loan segment, to deliver instant, flexible loans on consumer friendly terms to salaried professionals and businessmen.\n\nThe data science team at LoanTap is building an underwriting layer to determine the creditworthiness of MSMEs as well as individuals.\n\nLoanTap deploys formal credit to salaried individuals and businesses 4 main financial instruments:\n\nPersonal Loan\nEMI Free Loan\nPersonal Overdraft\nAdvance Salary Loan\nThis case study will focus on the underwriting process behind Personal Loan only\n\nProblem Statement:\n\nGiven a set of attributes for an Individual, determine if a credit line should be extended to them. If so, what should the repayment terms be in business recommendations?\n\nDataset: LoanTapData.csv\n\nData dictionary:\n\nloan_amnt : The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n\nterm : The number of payments on the loan. Values are in months and can be either 36 or 60.\n\nint_rate : Interest Rate on the loan\n\ninstallment : The monthly payment owed by the borrower if the loan originates.\n\ngrade : LoanTap assigned loan grade\n\nsub_grade : LoanTap assigned loan subgrade\n\nemp_title :The job title supplied by the Borrower when applying for the loan.*\n\nemp_length : Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.\n\nhome_ownership : The home ownership status provided by the borrower during registration or obtained from the credit report.\n\nannual_inc : The self-reported annual income provided by the borrower during registration.\n\nverification_status : Indicates if income was verified by LoanTap, not verified, or if the income source was verified\n\nissue_d : The month which the loan was funded\n\nloan_status : Current status of the loan - Target Variable\n\npurpose : A category provided by the borrower for the loan request.\n\ntitle : The loan title provided by the borrower\n\ndti : A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LoanTap loan, divided by the borrower’s self-reported monthly income.\n\nearliest_cr_line :The month the borrower's earliest reported credit line was opened\n\nopen_acc : The number of open credit lines in the borrower's credit file.\n\npub_rec : Number of derogatory public records\n\nrevol_bal : Total credit revolving balance\n\nrevol_util : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.\n\ntotal_acc : The total number of credit lines currently in the borrower's credit file\n\ninitial_list_status : The initial listing status of the loan. Possible values are – W, F\n\napplication_type : Indicates whether the loan is an individual application or a joint application with two co-borrowers\n\nmort_acc : Number of mortgage accounts.\n\npub_rec_bankruptcies : Number of public record bankruptcies\n\nAddress: Address of the individual\n\nConcept Used:\n\nExploratory Data Analysis\nFeature Engineering\nLogistic Regression\nPrecision Vs Recall Tradeoff\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniteshchawla%2Floantap-logisticregression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fniteshchawla%2Floantap-logisticregression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniteshchawla%2Floantap-logisticregression/lists"}