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