--- 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-* ---