australian: Australian Credit Approval Dataset

Description Usage Format Details Source Examples

Description

This file concerns credit card applications of 690 households.

Usage

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Format

This data set has been split into two components for the convenience of the model training.

data.frame-object X consists of with 6 numerical and 8 categorical attributes. The labels have been changed for the convenience of the statistical algorithms. For example, attribute 4 originally had 3 labels p,g,gg and these have been changed to labels 1,2,3.

Factor y indicates whether the application has been Accepted or Rejected

The training set AusCredit.tr contains a randomly selected set of 400 subjects, and AusCredit.te contains the remaining 290 subjects. AusCredit contains all 690 objects.

Details

All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.

This dataset is interesting because there is a good mix of attributes – continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.

Source

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Examples

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Example output

Loading required package: kernlab
     X1                X2              X3               X4       
 Mode :logical   Min.   :13.75   Min.   : 0.000   Min.   :1.000  
 FALSE:222       1st Qu.:22.67   1st Qu.: 1.000   1st Qu.:2.000  
 TRUE :468       Median :28.62   Median : 2.750   Median :2.000  
                 Mean   :31.57   Mean   : 4.759   Mean   :1.767  
                 3rd Qu.:37.71   3rd Qu.: 7.207   3rd Qu.:2.000  
                 Max.   :80.25   Max.   :28.000   Max.   :3.000  
       X5               X6              X7             X8         
 Min.   : 1.000   Min.   :1.000   Min.   : 0.000   Mode :logical  
 1st Qu.: 4.000   1st Qu.:4.000   1st Qu.: 0.165   FALSE:329      
 Median : 8.000   Median :4.000   Median : 1.000   TRUE :361      
 Mean   : 7.372   Mean   :4.693   Mean   : 2.223                  
 3rd Qu.:10.000   3rd Qu.:5.000   3rd Qu.: 2.625                  
 Max.   :14.000   Max.   :9.000   Max.   :28.500                  
     X9               X10          X11               X12             X13      
 Mode :logical   Min.   : 0.0   Mode :logical   Min.   :1.000   Min.   :   0  
 FALSE:395       1st Qu.: 0.0   FALSE:374       1st Qu.:2.000   1st Qu.:  80  
 TRUE :295       Median : 0.0   TRUE :316       Median :2.000   Median : 160  
                 Mean   : 2.4                   Mean   :1.929   Mean   : 184  
                 3rd Qu.: 3.0                   3rd Qu.:2.000   3rd Qu.: 272  
                 Max.   :67.0                   Max.   :3.000   Max.   :2000  
      X14          
 Min.   :     1.0  
 1st Qu.:     1.0  
 Median :     6.0  
 Mean   :  1018.4  
 3rd Qu.:   396.5  
 Max.   :100001.0  
Accepted Rejected 
     307      383 

SVMMaj documentation built on May 29, 2017, 5:13 p.m.

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