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https://github.com/niteshchawla/loantap-logisticregression

Given 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?
https://github.com/niteshchawla/loantap-logisticregression

confusion-matrix encoding feature-engineering featureimportance hyperparameter-tuning logistic-regression machine-learning numpy pandas-python precision-recall roc-auc-curve sklearn-metrics smote-sampling

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Given 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?

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README 

        
# LoanTap-LogisticRegression

Context:



LoanTap 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.



The data science team at LoanTap is building an underwriting layer to determine the creditworthiness of MSMEs as well as individuals.



LoanTap deploys formal credit to salaried individuals and businesses 4 main financial instruments:



Personal Loan

EMI Free Loan

Personal Overdraft

Advance Salary Loan

This case study will focus on the underwriting process behind Personal Loan only



Problem Statement:



Given 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?



Dataset: LoanTapData.csv



Data dictionary:



loan_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.



term : The number of payments on the loan. Values are in months and can be either 36 or 60.



int_rate : Interest Rate on the loan



installment : The monthly payment owed by the borrower if the loan originates.



grade : LoanTap assigned loan grade



sub_grade : LoanTap assigned loan subgrade



emp_title :The job title supplied by the Borrower when applying for the loan.*



emp_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.



home_ownership : The home ownership status provided by the borrower during registration or obtained from the credit report.



annual_inc : The self-reported annual income provided by the borrower during registration.



verification_status : Indicates if income was verified by LoanTap, not verified, or if the income source was verified



issue_d : The month which the loan was funded



loan_status : Current status of the loan - Target Variable



purpose : A category provided by the borrower for the loan request.



title : The loan title provided by the borrower



dti : 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.



earliest_cr_line :The month the borrower's earliest reported credit line was opened



open_acc : The number of open credit lines in the borrower's credit file.



pub_rec : Number of derogatory public records



revol_bal : Total credit revolving balance



revol_util : Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.



total_acc : The total number of credit lines currently in the borrower's credit file



initial_list_status : The initial listing status of the loan. Possible values are – W, F



application_type : Indicates whether the loan is an individual application or a joint application with two co-borrowers



mort_acc : Number of mortgage accounts.



pub_rec_bankruptcies : Number of public record bankruptcies



Address: Address of the individual



Concept Used:



Exploratory Data Analysis

Feature Engineering

Logistic Regression

Precision Vs Recall Tradeoff