German Credit Data

Context

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below.

Content

It is almost impossible to understand the original dataset due to its complicated system of categories and symbols. Thus, I wrote a small Python script to convert it into a readable CSV file. The column names were also given in German originally. So, they are replaced by English names while processing. The attributes and their details in English are given below:

1. Status - Categorical (Ordinal)
2. Duration - Numerical
3. Credit History - Categorical (Nominal)
4. Purpose - Categorical (Nominal)
5. Amount - Numerical
6. Savings - Categorical (Ordinal)
7. Employment Duration - Categorical (Ordinal)
8. Installment Rate - Categorical (Ordinal)
9. Personal Status Sex - Categorical (Nominal)
10. Other Debtors - Categorical (Nominal)
11. Present Residence - Categorical (Ordinal)
12. Property - Categorical (Nominal)
13. Age - Numerical
14. Other Installment Plans - Categorical (Nominal)
15. Housing - Categorical (Nominal)
16. Number Credits - Categorical (Ordinal)
17. Job - Categorical (Nominal)
18. People Liable - Categorical (Ordinal)
19. Telephone - Categorical (Nominal)
20. Foreign Worker - Categorical (Nominal)
21. Credit Risk - Binary Target Variable

Acknowledgements

Source : UCI