- German Credit Data Set Arff Download Free
- German Credit Data Set Arff Downloads
- German Credit Data Set Arff Download Software
The resources for this dataset can be found at https://www.openml.org/d/31
German Credit Data Set Arff Firefighter Resume. “Bad” and “Good”. We can see above (code for Figure ) that the German credit data is a case of unbalanced dataset with of the individuals being classified as having good credit. Therefore, the accuracy of a classification model should be superior to, which would be the accuracy of a. Source: Professor Dr. Hans Hofmann Institut f'ur Statistik und 'Okonometrie Universit'at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 Data Set Information: Two datasets are p. This repository contains the Analysis and Visualization of the German Credit Dataset. It predicts the jobs in which the German credit seekers were indulged in and hence, were most unsatisfied with the salaries that they were getting at that time using the input features like- Credit Amount, Age, Housing and Duration of loan. Or copy & paste this link into an email or IM.
German Credit Data Set Arff Download Free
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:
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
- Status of existing checking account, in Deutsche Mark.
- Duration in months
- Credit history (credits taken, paid back duly, delays, critical accounts)
- Purpose of the credit (car, television,…)
- Credit amount
- Status of savings account/bonds, in Deutsche Mark.
- Present employment, in number of years.
- Installment rate in percentage of disposable income
- Personal status (married, single,…) and sex
- Other debtors / guarantors
- Present residence since X years
- Property (e.g. real estate)
- Age in years
- Other installment plans (banks, stores)
- Housing (rent, own,…)
- Number of existing credits at this bank
- Job
- Number of people being liable to provide maintenance for
- Telephone (yes,no)
- Foreign worker (yes,no)
Attribute Details:
German Credit Data Set Arff Downloads
| Name | Type | Description |
|---|---|---|
| checking_account_status | string | Status of existing checking account (A11: < 0 DM, A12: 0 <= x < 200 DM, A13 : >= 200 DM / salary assignments for at least 1 year, A14 : no checking account) |
| duration | integer | Duration in month |
| credit_history | string | 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) |
| purpose | string | Purpose of Credit (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) |
| credit_amount | float | |
| savings | string | Savings in accounts/bonds (A61 : < 100 DM, A62 : 100 <= x < 500 DM, A63 : 500 <= x < 1000 DM, A64 : >= 1000 DM, A65 : unknown/ no savings account |
| present_employment | string | A71 : unemployed, A72 : < 1 year, A73 : 1 <= x < 4 years, A74 : 4 <= x < 7 years, A75 : .. >= 7 years |
| installment_rate | float | Installment Rate in percentage of disposable income |
| personal | string | Personal Marital Status and Sex (A91 : male : divorced/separated, A92 : female : divorced/separated/married, A93 : male : single, A94 : male : married/widowed, A95 : female : single) |
| other_debtors | string | A101 : none, A102 : co-applicant, A103 : guarantor |
| present_residence | float | Present residence since |
| property | string | 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 |
| age | float | Age in years |
| other_installment_plans | string | A141 : bank, A142 : stores, A143 : none |
| customer_type | integer | Predictor Class: 1=Good, 2=Bad |
German Credit Data Set Arff Download Software
Showing 15 out of 21 attributes. Download attribute CSV for full details