Credit Risk Modelling

Investigate and model the creditworthiness of applicants based on the German Credit Data Set from the UCI Machine Learning Repository.

finance

About

This data details the creditworthiness of applicants based on the German Credit Data Set from the UCI Machine Learning Repository.

Data

credit_personal.csv

credit_personal
Data Dictionary
variable description
credit_amount Credit amount in Deutsche Mark (DEM)
age Age in years
personal_status Marriage status and sex
job Type of job
employment Years since the present employment started
savings_status Amount in DEM in savings account/bonds
housing Housing status
residence_since Present residence since
property_magnitude Property type
num_dependents Number of dependents
own_telephone If an applicant owns a telephone
foreign_worker If an applicant is a foreign worker

credit_financial.csv

credit_financial
Data Dictionary
variable description
checking Status of existing checking account
duration Duration of credit in month
credit_history Credit history
purpose Purpose for credit
installment_commitment Installment rate in percentage of disposable income
other_parties Other debtors / guarantors
other_payment_plans Other installment plans
existing_credits Number of existing credits at this bank
class Classification of good (1) or bad (2) credit risk