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Datasets:
mstz
/
german

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
1K - 10K
Tags:
tabular_classification
binary_classification
multiclass_classification
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
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Auto-converted to Parquet
checking_account_status
int64
account_life_in_months
int64
credit_status
int64
loan_purpose
string
current_credit
int64
current_savings
int64
employed_since
int64
installment_rate_percentage
int64
is_male
bool
marital_status
int64
guarantors
int64
years_living_in_current_residence
int64
age
int64
installment_plans
int64
housing_status
int64
nr_credit_accounts_in_bank
int64
job_status
int64
number_of_people_in_support
int64
has_registered_phone_number
int64
is_foreign
bool
loan_granted
int64
1
6
4
radio/television
1,169
0
4
4
false
1
0
4
67
2
2
2
2
1
1
false
0
2
48
2
radio/television
5,951
1
2
2
true
0
0
2
22
2
2
1
2
1
0
false
1
0
12
4
education
2,096
1
3
2
false
1
0
3
49
2
2
1
1
2
0
false
0
1
42
2
furniture/equipment
7,882
1
3
2
false
1
2
4
45
2
0
1
2
2
0
false
0
1
24
3
new car
4,870
1
2
3
false
1
0
4
53
2
0
2
2
2
0
false
1
0
36
2
education
9,055
0
2
2
false
1
0
4
35
2
0
1
1
2
1
false
0
0
24
2
furniture/equipment
2,835
3
4
3
false
1
0
4
53
2
2
1
2
1
0
false
0
2
36
2
used car
6,948
1
2
2
false
1
0
2
35
2
1
1
3
1
1
false
0
0
12
2
radio/television
3,059
4
3
2
false
0
0
4
61
2
2
1
1
1
0
false
0
2
30
4
new car
5,234
1
0
4
false
2
0
2
28
2
2
2
3
1
0
false
1
2
12
2
new car
1,295
1
1
3
true
0
0
1
25
2
1
1
2
1
0
false
1
1
48
2
business
4,308
1
1
3
true
0
0
4
24
2
1
1
2
1
0
false
1
2
12
2
radio/television
1,567
1
2
1
true
0
0
1
22
2
2
1
2
1
1
false
0
1
24
4
new car
1,199
1
4
4
false
1
0
4
60
2
2
2
1
1
0
false
1
1
15
2
new car
1,403
1
2
2
true
0
0
4
28
2
1
1
2
1
0
false
0
1
24
2
radio/television
1,282
2
2
4
true
0
0
2
32
2
2
1
1
1
0
false
1
0
24
4
radio/television
2,424
0
4
4
false
1
0
4
53
2
2
2
2
1
0
false
0
1
30
0
business
8,072
0
1
2
false
1
0
3
25
0
2
3
2
1
0
false
0
2
24
2
used car
12,579
1
4
4
true
0
0
2
44
2
0
1
3
1
1
false
1
0
24
2
radio/television
3,430
3
4
3
false
1
0
2
31
2
2
1
2
2
1
false
0
0
9
4
new car
2,134
1
2
4
false
1
0
4
48
2
2
3
2
1
1
false
0
1
6
2
radio/television
2,647
3
2
2
false
1
0
3
44
2
1
1
2
2
0
false
0
1
10
4
new car
2,241
1
1
1
false
1
0
3
48
2
1
2
1
2
0
true
0
2
12
4
used car
1,804
2
1
3
false
1
0
4
44
2
2
1
2
1
0
false
0
0
10
4
furniture/equipment
2,069
0
2
2
false
2
0
1
26
2
2
2
2
1
0
true
0
1
6
2
furniture/equipment
1,374
1
2
1
false
1
0
2
36
0
2
1
1
1
1
false
0
0
6
0
radio/television
426
1
4
4
false
2
0
4
39
2
2
1
1
1
0
false
0
3
12
1
radio/television
409
4
2
3
true
0
0
3
42
2
1
2
2
1
0
false
0
2
7
2
radio/television
2,415
1
2
3
false
1
2
2
34
2
2
1
2
1
0
false
0
1
60
3
business
6,836
1
4
3
false
1
0
4
63
2
2
2
2
1
1
false
1
2
18
2
business
1,913
4
1
3
false
2
0
3
36
0
2
1
2
1
1
false
0
1
24
2
furniture/equipment
4,020
1
2
2
false
1
0
2
27
1
2
1
2
1
0
false
0
2
18
2
new car
5,866
2
2
2
false
1
0
2
30
2
2
2
2
1
1
false
0
0
12
4
business
1,264
0
4
4
false
1
0
4
57
2
1
1
1
1
0
false
0
3
12
2
furniture/equipment
1,474
1
1
4
true
0
0
1
33
0
2
1
3
1
1
false
0
2
45
4
radio/television
4,746
1
1
4
false
1
0
2
25
2
2
2
1
1
0
false
1
0
48
4
education
6,110
1
2
1
false
1
0
3
31
0
0
1
2
1
1
false
0
3
18
2
radio/television
2,100
1
2
4
false
1
1
2
37
1
2
1
2
1
0
false
1
3
10
2
domestic appliances
1,225
1
2
2
false
1
0
2
37
2
2
1
2
1
1
false
0
2
9
2
radio/television
458
1
2
4
false
1
0
3
24
2
2
1
2
1
0
false
0
0
30
2
radio/television
2,333
3
4
4
false
1
0
2
30
0
2
1
3
1
0
false
0
2
12
2
radio/television
1,158
3
2
3
false
0
0
1
26
2
2
1
2
1
1
false
0
2
18
3
repairs
6,204
1
2
2
false
1
0
4
44
2
2
1
1
2
1
false
0
1
30
4
used car
6,187
2
3
1
false
2
0
4
24
2
1
2
2
1
0
false
0
1
48
4
used car
6,143
1
4
4
true
0
0
4
58
1
0
2
1
1
0
false
1
0
11
4
new car
1,393
1
1
4
true
0
0
4
35
2
2
2
3
1
0
false
0
0
36
2
radio/television
2,299
3
4
4
false
1
0
4
39
2
2
1
2
1
0
false
0
1
6
2
used car
1,352
3
0
1
true
0
0
2
23
2
1
1
0
1
1
false
0
0
11
4
new car
7,228
1
2
1
false
1
0
4
39
2
2
2
1
1
0
false
0
0
12
2
radio/television
2,073
2
2
4
true
0
1
2
28
2
2
1
2
1
0
false
0
2
24
3
furniture/equipment
2,333
0
1
4
false
1
0
2
29
0
2
1
1
1
0
false
0
2
27
3
used car
5,965
1
4
1
false
1
0
2
30
2
2
2
3
1
1
false
0
0
12
2
radio/television
1,262
1
2
3
false
1
0
2
25
2
2
1
2
1
0
false
0
0
18
2
used car
3,378
0
2
2
false
1
0
1
31
2
2
1
2
1
1
false
0
2
36
3
new car
2,225
1
4
4
false
1
0
4
57
0
0
2
2
1
1
false
1
0
6
1
new car
783
0
2
1
false
1
2
2
26
1
2
1
1
2
0
false
0
2
12
2
radio/television
6,468
0
0
2
false
1
0
1
52
2
2
1
3
1
1
false
1
0
36
4
radio/television
9,566
1
2
2
true
0
0
2
31
1
2
2
2
1
0
false
0
3
18
2
new car
1,961
1
4
3
true
0
0
2
23
2
2
1
3
1
0
false
0
1
36
4
furniture/equipment
6,229
1
1
4
true
0
1
4
23
2
1
2
1
1
1
false
1
2
9
2
business
1,391
1
2
2
false
2
0
1
27
0
2
1
2
1
1
false
0
2
15
4
radio/television
1,537
0
4
4
false
1
2
4
50
2
2
2
2
1
1
false
0
2
36
0
business
1,953
1
4
4
false
1
0
4
61
2
0
1
3
1
1
false
1
2
48
0
business
14,421
1
2
2
false
1
0
2
25
2
2
1
2
1
1
false
1
0
24
2
radio/television
3,181
1
1
4
true
0
0
4
26
2
2
1
2
1
1
false
0
0
27
2
repairs
5,190
0
4
4
false
1
0
4
48
2
2
4
2
2
1
false
0
0
12
2
radio/television
2,171
1
1
2
true
0
0
2
29
0
2
1
2
1
0
false
0
2
12
2
new car
1,007
4
2
4
false
2
0
1
22
2
2
1
2
1
0
false
0
0
36
2
education
1,819
1
2
4
false
1
0
4
37
1
0
1
2
1
1
false
1
0
36
2
radio/television
2,394
0
2
4
true
0
0
4
25
2
2
1
2
1
0
false
0
0
36
2
used car
8,133
1
2
1
true
0
0
2
30
0
2
1
2
1
0
false
0
0
7
4
radio/television
730
0
4
4
false
1
0
2
46
2
1
2
1
1
1
false
0
1
8
4
others
1,164
1
4
3
false
1
0
4
51
0
0
2
3
2
1
false
0
2
42
4
business
5,954
1
3
2
true
0
0
1
41
0
2
2
1
1
0
false
0
1
36
2
education
1,977
0
4
4
false
1
0
4
40
2
2
1
3
1
1
false
1
1
12
4
used car
1,526
1
4
4
false
1
0
4
66
2
0
2
3
1
0
false
0
1
42
2
radio/television
3,965
1
1
4
false
1
0
3
34
2
2
1
2
1
0
false
1
2
11
3
radio/television
4,771
1
3
2
false
1
0
4
51
2
2
1
2
1
0
false
0
0
54
0
used car
9,436
0
2
2
false
1
0
2
39
2
2
1
1
2
0
false
0
2
30
2
furniture/equipment
3,832
1
1
2
false
2
0
1
22
2
2
1
2
1
0
false
0
0
24
2
radio/television
5,943
0
1
1
true
0
0
1
44
2
2
2
2
1
1
false
1
0
15
2
radio/television
1,213
3
4
4
false
1
0
3
47
1
2
1
2
1
1
false
0
0
18
2
business
1,568
2
2
3
true
0
0
4
24
2
1
1
1
1
0
false
0
1
24
2
others
1,755
1
4
4
true
0
2
4
58
2
2
1
1
1
1
false
0
1
10
2
radio/television
2,315
1
4
3
false
1
0
4
52
2
2
1
1
1
0
false
0
0
12
4
business
1,412
1
2
4
true
0
2
2
29
2
2
2
3
1
1
false
0
2
18
4
furniture/equipment
1,295
1
1
4
true
0
0
1
27
2
2
2
2
1
0
false
0
2
36
2
education
12,612
2
2
1
false
1
0
4
47
2
0
1
2
2
1
false
1
1
18
2
new car
2,249
2
3
4
false
1
0
3
30
2
2
1
3
2
1
false
0
1
12
0
repairs
1,108
1
3
4
false
1
0
3
28
2
2
2
2
1
0
false
1
0
12
4
radio/television
618
1
4
4
false
1
0
4
56
2
2
1
2
1
0
false
0
1
12
4
used car
1,409
1
4
4
false
1
0
3
54
2
2
1
2
1
0
false
0
0
12
4
radio/television
797
0
4
4
true
0
0
3
33
0
2
1
1
2
0
false
1
3
24
4
furniture/equipment
3,617
0
4
4
false
1
1
4
20
2
1
2
2
1
0
false
0
2
12
2
new car
1,318
4
4
4
false
1
0
4
54
2
2
1
2
1
1
false
0
2
54
0
business
15,945
1
1
3
false
1
0
4
58
2
1
1
2
1
1
false
1
0
12
4
education
2,012
0
3
4
true
0
0
2
61
2
2
1
2
1
0
false
0
2
18
2
business
2,622
2
2
4
false
1
0
4
34
2
2
1
2
1
0
false
0
2
36
4
radio/television
2,337
1
4
4
false
1
0
4
36
2
2
1
2
1
0
false
0
2
20
3
used car
7,057
0
3
3
false
1
0
4
36
0
1
2
3
2
1
false
0
End of preview. Expand in Data Studio

German

The German dataset from the UCI ML repository. Dataset on loan grants to customers.

Configurations and tasks

Configuration Task Description
encoding Encoding dictionary showing original values of encoded features.
loan Binary classification Has the loan request been accepted?

Usage 

from datasets import load_dataset

dataset = load_dataset("mstz/german", "loan")["train"]

Features

Feature Type
checking_account_status int8
account_life_in_months int8
credit_status int8
loan_purpose string
current_credit int32
current_savings int8
employed_since int8
installment_rate_percentage int8
sex int8
marital_status string
guarantors int8
years_living_in_current_residence int8
age int8
installment_plans string
housing_status int8
nr_credit_accounts_in_bank int8
job_status int8
number_of_people_in_support int8
has_registered_phone_number int8
is_foreign int8
Downloads last month
24
Size of downloaded dataset files:
66 kB
Size of the auto-converted Parquet files:
23.8 kB
Number of rows:
1,000