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---
dataset_info:
features:
- name: credits_this_bank
dtype: int64
- name: present_res_since
dtype: int64
- name: duration_in_month
dtype: int64
- name: people_under_maintenance
dtype: int64
- name: installment_as_income_perc
dtype: int64
- name: age
dtype: int64
- name: credit_amount
dtype: int64
- name: account_check_status_0 <= ... < 200 DM
dtype: float64
- name: account_check_status_< 0 DM
dtype: float64
- name: account_check_status_>= 200 DM / salary assignments for at least 1 year
dtype: float64
- name: account_check_status_no checking account
dtype: float64
- name: credit_history_all credits at this bank paid back duly
dtype: float64
- name: credit_history_critical account/ other credits existing (not at this bank)
dtype: float64
- name: credit_history_delay in paying off in the past
dtype: float64
- name: credit_history_existing credits paid back duly till now
dtype: float64
- name: credit_history_no credits taken/ all credits paid back duly
dtype: float64
- name: purpose_(vacation - does not exist?)
dtype: float64
- name: purpose_business
dtype: float64
- name: purpose_car (new)
dtype: float64
- name: purpose_car (used)
dtype: float64
- name: purpose_domestic appliances
dtype: float64
- name: purpose_education
dtype: float64
- name: purpose_furniture/equipment
dtype: float64
- name: purpose_radio/television
dtype: float64
- name: purpose_repairs
dtype: float64
- name: purpose_retraining
dtype: float64
- name: 'savings_.. >= 1000 DM '
dtype: float64
- name: savings_... < 100 DM
dtype: float64
- name: savings_100 <= ... < 500 DM
dtype: float64
- name: 'savings_500 <= ... < 1000 DM '
dtype: float64
- name: savings_unknown/ no savings account
dtype: float64
- name: present_emp_since_.. >= 7 years
dtype: float64
- name: 'present_emp_since_... < 1 year '
dtype: float64
- name: present_emp_since_1 <= ... < 4 years
dtype: float64
- name: present_emp_since_4 <= ... < 7 years
dtype: float64
- name: present_emp_since_unemployed
dtype: float64
- name: 'gender_female '
dtype: float64
- name: 'gender_male '
dtype: float64
- name: personal_status_ divorced/separated
dtype: float64
- name: personal_status_ divorced/separated/married
dtype: float64
- name: personal_status_ married/widowed
dtype: float64
- name: personal_status_ single
dtype: float64
- name: 'property_if not A121 : building society savings agreement/ life insurance'
dtype: float64
- name: 'property_if not A121/A122 : car or other, not in attribute 6'
dtype: float64
- name: property_real estate
dtype: float64
- name: property_unknown / no property
dtype: float64
- name: other_installment_plans_bank
dtype: float64
- name: other_installment_plans_none
dtype: float64
- name: other_installment_plans_stores
dtype: float64
- name: housing_for free
dtype: float64
- name: housing_own
dtype: float64
- name: housing_rent
dtype: float64
- name: job_management/ self-employed/ highly qualified employee/ officer
dtype: float64
- name: job_skilled employee / official
dtype: float64
- name: job_unemployed/ unskilled - non-resident
dtype: float64
- name: job_unskilled - resident
dtype: float64
- name: telephone_none
dtype: float64
- name: 'telephone_yes, registered under the customers name '
dtype: float64
- name: other_debtors_co-applicant
dtype: float64
- name: other_debtors_guarantor
dtype: float64
- name: other_debtors_none
dtype: float64
- name: foreign_worker_no
dtype: float64
- name: foreign_worker_yes
dtype: float64
- name: young
dtype: float64
- name: adult
dtype: float64
- name: senior
dtype: float64
- name: default
dtype: int64
splits:
- name: train
num_bytes: 428800
num_examples: 800
- name: test
num_bytes: 107200
num_examples: 200
download_size: 87613
dataset_size: 536000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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