A credit scoring data set that can be used to predict defaults on consumer loans in the German market.
data(german.credit)The data contains 1000 observations (700 good loans, 300 bad loans) and the following variables:
Account_status: a factor with four levels representing the
amount of money in the account or "no chcking account".
Duration: a continuous variable, the duration in months.
Credit_history: a factor with five levels representing possible
credit history backgrounds.
Purpose: a factor with ten levels representing possible reasons
for taking out a loan.
Credit_amount: a continuous variable.
Savings_bonds: a factor with five levels representing amount of
money available in savings and bonds or
"unknown / no savings account".
Present_employment_since: a factor with five levels representing
the length of tenure in the current employment or "unemployed".
Installment_rate: a continuous variable, the installment rate in
percentage of disposable income.
Other_debtors_guarantors: a factor with levels "none",
"co-applicant" and "guarantor".
Resident_since: a continuous variable, number of years in the
current residence.
Property: a factor with four levels describing the type of
property to be bought or "unknown / no property".
Age: a continuous variable, the age in years.
Other_installment_plans: a factor with levels "bank",
"none" and "stores".
Housing: a factor with levels "rent", "own" and
"for free".
Existing_credits: a continuous variable, the number of existing
credit lines at this bank.
Job: a factor with four levels for different job descriptions.
People_maintenance_for: a continuous variable, the number of
people being liable to provide maintenance for.
Telephone: a factor with levels "none" and "yes".
Foreign_worker: a factor with levels "no" and
"yes".
Credit_risk: a factor with levels "BAD" and
"GOOD".
Gender: a factor with levels "Male" and
"Female".
UCI Machine Learning Repository:
https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)