size_categories:1K<n<10K format:parquet modality:tabular library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: status of existing checking account
    dtype:
      class_label:
        names:
          '0': < 0 DM
          '1': 0 <= ... < 200 DM
          '2': '>= 200 DM / salary assignments for at least 1 year'
          '3': no checking account
  - name: duration in month
    dtype: float32
  - name: credit history
    dtype:
      class_label:
        names:
          '0': no credits taken / all credits paid back duly
          '1': all credits at this bank paid back duly
          '2': existing credits paid back duly till now
          '3': delay in paying off in the past
          '4': critical account / other credits existing (not at this bank)
  - name: purpose
    dtype:
      class_label:
        names:
          '0': car (new)
          '1': car (used)
          '2': furniture/equipment
          '3': radio/television
          '4': domestic appliances
          '5': repairs
          '6': education
          '7': vacation
          '8': retraining
          '9': business
          '10': others
  - name: credit amount
    dtype: float32
  - name: savings account/bonds
    dtype:
      class_label:
        names:
          '0': < 100 DM
          '1': 100 <= ... < 500 DM
          '2': 500 <= ... < 1000 DM
          '3': '>= 1000 DM'
          '4': unknown / no savings account
  - name: present employment since
    dtype:
      class_label:
        names:
          '0': unemployed
          '1': < 1 year
          '2': 1 <= ... < 4 years
          '3': 4 <= ... < 7 years
          '4': '>= 7 years'
  - name: installment rate in percentage of disposable income
    dtype: float32
  - name: personal status and sex
    dtype:
      class_label:
        names:
          '0': 'male: divorced/separated'
          '1': 'female: divorced/separated/married'
          '2': 'male: single'
          '3': 'male: married/widowed'
          '4': 'female: single'
  - name: other debtors / guarantors
    dtype:
      class_label:
        names:
          '0': none
          '1': co-applicant
          '2': guarantor
  - name: present residence since
    dtype: float32
  - name: property
    dtype:
      class_label:
        names:
          '0': real estate
          '1': building society savings agreement / life insurance
          '2': car or other, not in attribute 6
          '3': unknown / no property
  - name: age in years
    dtype: float32
  - name: other installment plans
    dtype:
      class_label:
        names:
          '0': bank
          '1': stores
          '2': none
  - name: housing
    dtype:
      class_label:
        names:
          '0': rent
          '1': own
          '2': for free
  - name: number of existing credits at this bank
    dtype: float32
  - name: job
    dtype:
      class_label:
        names:
          '0': unemployed / unskilled - non-resident
          '1': unskilled - resident
          '2': skilled employee / official
          '3': management / self-employed / highly qualified employee / officer
  - name: number of people being liable to provide maintenance for
    dtype: float32
  - name: telephone
    dtype:
      class_label:
        names:
          '0': none
          '1': yes, registered under the customer’s name
  - name: foreign worker
    dtype:
      class_label:
        names:
          '0': 'yes'
          '1': 'no'
  - name: class
    dtype:
      class_label:
        names:
          '0': good
          '1': bad
  splits:
  - name: train
    num_bytes: 140000
    num_examples: 1000
  download_size: 27173
  dataset_size: 140000
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
