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license: cc0-1.0
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---
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---
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license: cc0-1.0
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---
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## Default of Credit Card Clients Dataset
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The following was retrieved from [UCI machine learning repository](https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients).
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**Dataset Information**
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This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.
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**Content**
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There are 25 variables:
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ID: ID of each client
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LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
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SEX: Gender (1=male, 2=female)
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EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
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MARRIAGE: Marital status (1=married, 2=single, 3=others)
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AGE: Age in years
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PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, … 8=payment delay for eight months, 9=payment delay for nine months and above)
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PAY_2: Repayment status in August, 2005 (scale same as above)
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PAY_3: Repayment status in July, 2005 (scale same as above)
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PAY_4: Repayment status in June, 2005 (scale same as above)
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PAY_5: Repayment status in May, 2005 (scale same as above)
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PAY_6: Repayment status in April, 2005 (scale same as above)
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BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
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BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
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BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
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BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
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BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
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BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
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PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
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PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
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PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
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PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
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PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
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PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
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default.payment.next.month: Default payment (1=yes, 0=no)
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**Inspiration**
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Some ideas for exploration:
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How does the probability of default payment vary by categories of different demographic variables?
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Which variables are the strongest predictors of default payment?
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**Acknowledgements**
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Any publications based on this dataset should acknowledge the following:
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Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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