GC:
German Credit Screening Data
Description
Loans are an assest for the banks! However, not all the loans are promptly returned and it is thus important for a bank to build a classification model which can identify the loan defaulters from those who complete the loan tenure.
Format
A data frame with 1000 observations on the following 21 variables.
checking
- Status of existing checking account
duration
- Duration in month
history
- Credit history
purpose
- Purpose of loan
amount
- Credit amount
savings
- Savings account or bonds
employed
- Present employment since
installp
- Installment rate in percentage of disposable income
marital
- Personal status and sex
coapp
- Other debtors or guarantors
resident
- Present residence since
property
- Property
age
- Age in years
other
- Other installment plans
housing
- Housing
existcr
- Number of existing credits at this bank
job
- Job
depends
- Number of people being liable to provide maintenance for
telephon
- Telephone
foreign
- foreign worker
good_bad
- Loan Defaulter
Source
http://www.stat.auckland.ac.nz/~reilly/credit-g.arff and http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)References
cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf