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Credit-Scoring-Notebook

Predicting POD on Financial distress customer data

Variables in the dataset:

SeriousDlqin2yrs: Person experienced 90 days past due delinquency or worse (Target variable / label)

RevolvingUtilizationOfUnsecuredLines: Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits

age Age of borrower in years

NumberOfTime30-59DaysPastDueNotWorse: Number of times borrower has been 30-59 days past due but no worse in the last 2 years.

DebtRatio: Monthly debt payments, alimony,living costs divided by monthy gross income

MonthlyIncome: Monthly income

NumberOfOpenCreditLinesAndLoans: Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards)

NumberOfTimes90DaysLate: Number of times borrower has been 90 days or more past due.

NumberRealEstateLoansOrLines: Number of mortgage and real estate loans including home equity lines of credit

NumberOfTime60-89DaysPastDueNotWorse: Number of times borrower has been 60-89 days past due but no worse in the last 2 years.

NumberOfDependents: Number of dependents in family excluding themselves (spouse, children etc.)

Methodology and Prediction accruacy

Random Forest classifier with quantile based approach

80%

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Predicting POD on Financial distress customer data

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