upl: Unsecured Personal Loans (UPLs)
In gmlang/loans: Data used in books I published

This dataset contains all the relevant information collected at the time of loan application, as well as some credit bureau data.

upl

A data frame with 7250 rows and 17 variables:

purpose: 0 Standard Credit Card. 1 Balance Transfer.
age: Applicant age (in years).
marital: 1 if applicant is married.
employment: 1 Employed Full Time. 2 Employed Part time. 3 Self Employed. 4 Temporary Employment. 5 Homemaker. 6 Retired.
annual_income: Annual Gross Income in British Pounds.
debt_to_income: Ratio of current debt to annual income.
market_value: Market value of property.
own_property: 1 if applicant is a property owner.
late_repayments: 1 if applicant delayed any debt repayment in the past year.
repossess: 1 if applicant has had his/her properties repossessed in the past.
conviction: 1 if applicant has ever been convicted.
bankruptcy: 1 if applicant has declared bankruptcy or made Individual Voluntary Arrangements.
unspent_convictions: 1 if applicant has unspent convictions.
credit_applications: Number of applications for credit in the past 12 months.
credit_line_age: Age of longest credit line (in months).
exist_customer: 1 if applicant is an existing customer.
bad: 1 Bad customer. 0 Good customer. A bad customer is someone who has missed three or more payments during the first year of the loan.