AustralianCredit: A Credit Approval Dataset

In OptimClassifier: Create the Best Train for Classification Models

Description

This dataset concerns credit card applications and represent positive and negative instances of people who were and were not granted credit. It has served as an important test data set for several credit-scoring algorithms. This dataset was introduced by Quinlan (1987).

Usage

data("AustralianCredit")

Format

A data frame with 690 observations on the following 15 variables.

X1

a factor with levels 0 and 1

X2

a numeric vector

X3

a numeric vector

X4

a factor with 3 levels

X5

a factor with 14 levels

X6

a factor with 9 levels

X7

a numeric vector

X8

a factor with levels 0 and 1

X9

a factor with levels 0 and 1

X10

a numeric vector

X11

a factor with levels 0 and 1

X12

a factor with 3 levels

X13

a numeric vector

X14

a numeric vector

Y

a factor with levels 0 and 1

References

Lichman, M. (2013). UCI machine learning repository. Quinlan,R. (1987). "Simplifying decision trees", Int J Man-Machine Studies 27, pp. 221-234.

Examples

data(AustralianCredit)

## See a general view of a dataset
summary(AustralianCredit)

## Plot a response variable
plot(AustralianCredit$Y)

Example output

 X1            X2              X3         X4            X5            X6     
 0:222   Min.   :13.75   Min.   : 0.000   1:163   8      :146   4      :408  
 1:468   1st Qu.:22.67   1st Qu.: 1.000   2:525   11     : 78   8      :138  
         Median :28.62   Median : 2.750   3:  2   9      : 64   5      : 59  
         Mean   :31.57   Mean   : 4.759           3      : 59   1      : 57  
         3rd Qu.:37.71   3rd Qu.: 7.207           6      : 54   3      :  8  
         Max.   :80.25   Max.   :28.000           1      : 53   9      :  8  
                                                  (Other):236   (Other): 12  
       X7         X8      X9           X10       X11     X12          X13      
 Min.   : 0.000   0:329   0:395   Min.   : 0.0   0:374   1: 57   Min.   :   0  
 1st Qu.: 0.165   1:361   1:295   1st Qu.: 0.0   1:316   2:625   1st Qu.:  80  
 Median : 1.000                   Median : 0.0           3:  8   Median : 160  
 Mean   : 2.223                   Mean   : 2.4                   Mean   : 184  
 3rd Qu.: 2.625                   3rd Qu.: 3.0                   3rd Qu.: 272  
 Max.   :28.500                   Max.   :67.0                   Max.   :2000  
                                                                               
      X14           Y      
 Min.   :     1.0   0:383  
 1st Qu.:     1.0   1:307  
 Median :     6.0          
 Mean   :  1018.4          
 3rd Qu.:   396.5          
 Max.   :100001.0          
                           

OptimClassifier documentation built on Jan. 14, 2020, 5:10 p.m.