openml-datasets

The resources for this dataset can be found at https://www.openml.org/d/31

Author: Dr. Hans Hofmann
Source: UCI - 1994
Please cite: UCI

German Credit data
This dataset classifies people described by a set of attributes as good or bad credit risks.

This dataset comes with a cost matrix:

      Good  Bad (predicted)  
Good   0    1   (actual)  
Bad    5    0  

It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1).

Attribute description

1. Status of existing checking account, in Deutsche Mark.
2. Duration in months
3. Credit history (credits taken, paid back duly, delays, critical accounts)
4. Purpose of the credit (car, television,…)
5. Credit amount
6. Status of savings account/bonds, in Deutsche Mark.
7. Present employment, in number of years.
8. Installment rate in percentage of disposable income
9. Personal status (married, single,…) and sex
10. Other debtors / guarantors
11. Present residence since X years
12. Property (e.g. real estate)
13. Age in years
14. Other installment plans (banks, stores)
15. Housing (rent, own,…)
16. Number of existing credits at this bank
17. Job
18. Number of people being liable to provide maintenance for
19. Telephone (yes,no)
20. Foreign worker (yes,no)