Datasets:

AiresPucrs
/

german-credit-data

Languages:

English

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n<1K

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train · 1k rows

Age int64 19 75	Sex stringclasses 2 values	Job int64 0 3	Housing stringclasses 3 values	Saving accounts stringclasses 4 values	Checking account stringclasses 3 values	Credit amount int64 250 18.4k	Duration int64 4 72	Purpose stringclasses 8 values	Risk stringclasses 2 values
67	male	2	own	null	little	1,169	6	radio/TV	good
22	female	2	own	little	moderate	5,951	48	radio/TV	bad
49	male	1	own	little	null	2,096	12	education	good
45	male	2	free	little	little	7,882	42	furniture/equipment	good
53	male	2	free	little	little	4,870	24	car	bad
35	male	1	free	null	null	9,055	36	education	good
53	male	2	own	quite rich	null	2,835	24	furniture/equipment	good
35	male	3	rent	little	moderate	6,948	36	car	good
61	male	1	own	rich	null	3,059	12	radio/TV	good
28	male	3	own	little	moderate	5,234	30	car	bad
25	female	2	rent	little	moderate	1,295	12	car	bad
24	female	2	rent	little	little	4,308	48	business	bad
22	female	2	own	little	moderate	1,567	12	radio/TV	good
60	male	1	own	little	little	1,199	24	car	bad
28	female	2	rent	little	little	1,403	15	car	good
32	female	1	own	moderate	little	1,282	24	radio/TV	bad
53	male	2	own	null	null	2,424	24	radio/TV	good
25	male	2	own	null	little	8,072	30	business	good
44	female	3	free	little	moderate	12,579	24	car	bad
31	male	2	own	quite rich	null	3,430	24	radio/TV	good
48	male	2	own	little	null	2,134	9	car	good
44	male	2	rent	quite rich	little	2,647	6	radio/TV	good
48	male	1	rent	little	little	2,241	10	car	good
44	male	2	own	moderate	moderate	1,804	12	car	good
26	male	2	own	null	null	2,069	10	furniture/equipment	good
36	male	1	own	little	little	1,374	6	furniture/equipment	good
39	male	1	own	little	null	426	6	radio/TV	good
42	female	2	rent	rich	rich	409	12	radio/TV	good
34	male	2	own	little	moderate	2,415	7	radio/TV	good
63	male	2	own	little	little	6,836	60	business	bad
36	male	2	own	rich	moderate	1,913	18	business	good
27	male	2	own	little	little	4,020	24	furniture/equipment	good
30	male	2	own	moderate	moderate	5,866	18	car	good
57	male	1	rent	null	null	1,264	12	business	good
33	female	3	own	little	rich	1,474	12	furniture/equipment	good
25	male	1	own	little	moderate	4,746	45	radio/TV	bad
31	male	2	free	little	null	6,110	48	education	good
37	male	2	own	little	rich	2,100	18	radio/TV	bad
37	male	2	own	little	rich	1,225	10	domestic appliances	good
24	male	2	own	little	moderate	458	9	radio/TV	good
30	male	3	own	quite rich	null	2,333	30	radio/TV	good
26	male	2	own	quite rich	moderate	1,158	12	radio/TV	good
44	male	1	own	little	moderate	6,204	18	repairs	good
24	male	2	rent	moderate	little	6,187	30	car	good
58	female	1	free	little	little	6,143	48	car	bad
35	female	3	own	little	null	1,393	11	car	good
39	male	2	own	quite rich	null	2,299	36	radio/TV	good
23	female	0	rent	quite rich	little	1,352	6	car	good
39	male	1	own	little	null	7,228	11	car	good
28	female	2	own	moderate	null	2,073	12	radio/TV	good
29	male	1	own	null	moderate	2,333	24	furniture/equipment	good
30	male	3	own	little	moderate	5,965	27	car	good
25	male	2	own	little	null	1,262	12	radio/TV	good
31	male	2	own	null	null	3,378	18	car	good
57	male	2	free	little	moderate	2,225	36	car	bad
26	male	1	own	null	null	783	6	car	good
52	male	3	own	null	moderate	6,468	12	radio/TV	bad
31	female	2	own	little	null	9,566	36	radio/TV	good
23	female	3	own	little	rich	1,961	18	car	good
23	female	1	rent	little	little	6,229	36	furniture/equipment	bad
27	male	2	own	little	moderate	1,391	9	business	good
50	male	2	own	null	moderate	1,537	15	radio/TV	good
61	male	3	free	little	moderate	1,953	36	business	bad
25	male	2	own	little	moderate	14,421	48	business	bad
26	female	2	own	little	null	3,181	24	radio/TV	good
48	male	2	own	null	null	5,190	27	repairs	good
29	female	2	own	little	null	2,171	12	radio/TV	good
22	male	2	own	rich	moderate	1,007	12	car	good
37	male	2	free	little	null	1,819	36	education	bad
25	female	2	own	null	null	2,394	36	radio/TV	good
30	female	2	own	little	null	8,133	36	car	good
46	male	1	rent	null	null	730	7	radio/TV	good
51	male	3	free	little	little	1,164	8	vacation/others	good
41	female	1	own	little	moderate	5,954	42	business	good
40	male	3	own	null	little	1,977	36	education	bad
66	male	3	free	little	little	1,526	12	car	good
34	male	2	own	little	little	3,965	42	radio/TV	bad
51	male	2	own	little	moderate	4,771	11	radio/TV	good
39	male	1	own	null	null	9,436	54	car	good
22	male	2	own	little	moderate	3,832	30	furniture/equipment	good
44	female	2	own	null	null	5,943	24	radio/TV	bad
47	male	2	own	quite rich	null	1,213	15	radio/TV	good
24	female	1	rent	moderate	null	1,568	18	business	good
58	female	1	own	little	little	1,755	24	vacation/others	good
52	male	1	own	little	little	2,315	10	radio/TV	good
29	female	3	own	little	null	1,412	12	business	good
27	female	2	own	little	moderate	1,295	18	furniture/equipment	good
47	male	2	free	moderate	moderate	12,612	36	education	bad
30	male	3	own	moderate	little	2,249	18	car	good
28	male	2	own	little	little	1,108	12	repairs	bad
56	male	2	own	little	null	618	12	radio/TV	good
54	male	2	own	little	little	1,409	12	car	good
33	female	1	own	null	null	797	12	radio/TV	bad
20	male	2	rent	null	rich	3,617	24	furniture/equipment	good
54	male	2	own	rich	moderate	1,318	12	car	good
58	male	2	rent	little	moderate	15,945	54	business	bad
61	female	2	own	null	null	2,012	12	education	good
34	male	2	own	moderate	moderate	2,622	18	business	good
36	male	2	own	little	moderate	2,337	36	radio/TV	good
36	male	3	rent	null	moderate	7,057	20	car	good

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German Credit Data

Overview

This dataset has information about 1000 individuals regarding their credit scores (bad = 0, good = 1), all set as a binary classification problem.

Dataset Details

The dataset is a smaller version of the original dataset. This data originally came from Statlog (German Credit Data)

Dataset Name: German Credit Risk - With Target)
Language: English
Total Size: 1000

The dataset consists of a data frame with the following columns:

Age [int64]
Sex [string]
Job [int64]
Housing [string]
Saving accounts [string]
Checking account [string]
Credit amount [int64]
Duration [int64]
Purpose [string]
Risk [string]

How to use

from datasets import load_dataset

dataset = load_dataset("AiresPucrs/german-credit-data", split = 'train')

License

The German Credit Risk dataset is licensed under the Creative Commons License CC0 1.0 Universal.

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Size of downloaded dataset files:

14 kB

Size of the auto-converted Parquet files:

14 kB

Number of rows:

1,000

German Credit Data

Overview

Dataset Details

Contents

How to use

License