El German Credit Dataset — 2:31 min#
2:31 min | Ultima modificación: Septiembre 28, 2021 | YouTube
En este dataset se clasifican personas de acuerdo con sus hábitos de pago como riesgosas o no riesgosas.
El dataset contiene un total de 1.000 instancias y 20 atributos, de los cuales, 17 son categóricos. No hay valores faltantes.
La variable de salida toma los siguientes valores:
1 - Good
2 - Bad
Los atributos y sus valores son los siguientes:
Attribute 1: (qualitative)
Status of existing checking account
A11 : ... < 0 DM
A12 : 0 <= ... < 200 DM
A13 : ... >= 200 DM /
salary assignments for at least 1 year
A14 : no checking account
Attribute 2: (numerical)
Duration in month
Attribute 3: (qualitative)
Credit history
A30 : no credits taken/
all credits paid back duly
A31 : all credits at this bank paid back duly
A32 : existing credits paid back duly till now
A33 : delay in paying off in the past
A34 : critical account/
other credits existing (not at this bank)
Attribute 4: (qualitative)
Purpose
A40 : car (new)
A41 : car (used)
A42 : furniture/equipment
A43 : radio/television
A44 : domestic appliances
A45 : repairs
A46 : education
A47 : (vacation - does not exist?)
A48 : retraining
A49 : business
A410 : others
Attribute 5: (numerical)
Credit amount
Attribute 6: (qualitative)
Savings account/bonds
A61 : ... < 100 DM
A62 : 100 <= ... < 500 DM
A63 : 500 <= ... < 1000 DM
A64 : .. >= 1000 DM
A65 : unknown/ no savings account
Attribute 7: (qualitative)
Present employment since
A71 : unemployed
A72 : ... < 1 year
A73 : 1 <= ... < 4 years
A74 : 4 <= ... < 7 years
A75 : .. >= 7 years
Attribute 8: (numerical)
Installment rate in percentage of disposable income
Attribute 9: (qualitative)
Personal status and sex
A91 : male : divorced/separated
A92 : female : divorced/separated/married
A93 : male : single
A94 : male : married/widowed
A95 : female : single
Attribute 10: (qualitative)
Other debtors / guarantors
A101 : none
A102 : co-applicant
A103 : guarantor
Attribute 11: (numerical)
Present residence since
Attribute 12: (qualitative)
Property
A121 : real estate
A122 : if not A121 : building society savings agreement/
life insurance
A123 : if not A121/A122 : car or other, not in attribute 6
A124 : unknown / no property
Attribute 13: (numerical)
Age in years
Attribute 14: (qualitative)
Other installment plans
A141 : bank
A142 : stores
A143 : none
Attribute 15: (qualitative)
Housing
A151 : rent
A152 : own
A153 : for free
Attribute 16: (numerical)
Number of existing credits at this bank
Attribute 17: (qualitative)
Job
A171 : unemployed/ unskilled - non-resident
A172 : unskilled - resident
A173 : skilled employee / official
A174 : management/ self-employed/
highly qualified employee/ officer
Attribute 18: (numerical)
Number of people being liable to provide maintenance for
Attribute 19: (qualitative)
Telephone
A191 : none
A192 : yes, registered under the customers name
Attribute 20: (qualitative)
foreign worker
A201 : yes
A202 : no
[1]:
import pandas as pd
df = pd.read_csv(
"https://raw.githubusercontent.com/jdvelasq/datalabs/master/datasets/german.csv",
)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 21 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 checking_balance 1000 non-null object
1 months_loan_duration 1000 non-null int64
2 credit_history 1000 non-null object
3 purpose 1000 non-null object
4 amount 1000 non-null int64
5 savings_balance 1000 non-null object
6 employment_length 1000 non-null object
7 installment_rate 1000 non-null int64
8 personal_status 1000 non-null object
9 other_debtors 1000 non-null object
10 residence_history 1000 non-null int64
11 property 1000 non-null object
12 age 1000 non-null int64
13 installment_plan 1000 non-null object
14 housing 1000 non-null object
15 existing_credits 1000 non-null int64
16 default 1000 non-null int64
17 dependents 1000 non-null int64
18 telephone 1000 non-null object
19 foreign_worker 1000 non-null object
20 job 1000 non-null object
dtypes: int64(8), object(13)
memory usage: 164.2+ KB
[2]:
df.head()
[2]:
| checking_balance | months_loan_duration | credit_history | purpose | amount | savings_balance | employment_length | installment_rate | personal_status | other_debtors | ... | property | age | installment_plan | housing | existing_credits | default | dependents | telephone | foreign_worker | job | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | < 0 DM | 6 | critical | radio/tv | 1169 | unknown | > 7 yrs | 4 | single male | none | ... | real estate | 67 | none | own | 2 | 1 | 1 | yes | yes | skilled employee |
| 1 | 1 - 200 DM | 48 | repaid | radio/tv | 5951 | < 100 DM | 1 - 4 yrs | 2 | female | none | ... | real estate | 22 | none | own | 1 | 2 | 1 | none | yes | skilled employee |
| 2 | unknown | 12 | critical | education | 2096 | < 100 DM | 4 - 7 yrs | 2 | single male | none | ... | real estate | 49 | none | own | 1 | 1 | 2 | none | yes | unskilled resident |
| 3 | < 0 DM | 42 | repaid | furniture | 7882 | < 100 DM | 4 - 7 yrs | 2 | single male | guarantor | ... | building society savings | 45 | none | for free | 1 | 1 | 2 | none | yes | skilled employee |
| 4 | < 0 DM | 24 | delayed | car (new) | 4870 | < 100 DM | 1 - 4 yrs | 3 | single male | none | ... | unknown/none | 53 | none | for free | 2 | 2 | 2 | none | yes | skilled employee |
5 rows × 21 columns