| UCICreditCard {creditmodel} | R Documentation |
UCI Credit Card data
Description
This research aimed at the case of customers's default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 24 variables as explanatory variables
Format
A data frame with 30000 rows and 26 variables.
Details
ID: Customer id
apply_date: This is a fake occur time.
LIMIT_BAL: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.
SEX: Gender (male; female).
EDUCATION: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others).
MARRIAGE: Marital status (1 = married; 2 = single; 3 = others).
AGE: Age (year) History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows:
PAY_0: the repayment status in September
PAY_2: the repayment status in August
PAY_3: ...
PAY_4: ...
PAY_5: ...
PAY_6: the repayment status in April The measurement scale for the repayment status is: -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. Amount of bill statement (NT dollar)
BILL_AMT1: amount of bill statement in September
BILL_AMT2: mount of bill statement in August
BILL_AMT3: ...
BILL_AMT4: ...
BILL_AMT5: ...
BILL_AMT6: amount of bill statement in April Amount of previous payment (NT dollar)
PAY_AMT1: amount paid in September
PAY_AMT2: amount paid in August
PAY_AMT3: ....
PAY_AMT4: ...
PAY_AMT5: ...
PAY_AMT6: amount paid in April
default.payment.next.month: default payment (Yes = 1, No = 0), as the response variable
Source
http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients