Credit: Credit approval (Frank and Asuncion, 2010)

In aCRM: Convenience functions for analytical Customer Relationship Management

Description

Credit contains credit card applications. The dataset has a good mix of continuous and categorical features.

Usage

data(Credit)

Format

A data frame with 653 observations, 15 predictors and a binary criterion variable called Response

Details

All observations with missing values are deleted.

Source

Frank, A. and Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

References

The original dataset can be downloaded at http://archive.ics.uci.edu/ml/datasets/Credit+Approval

Examples

data(Credit)
str(Credit)
table(Credit$Response)

Example output

aCRM 0.1.1
Type aCRMNews() to see the change log
'data.frame':	653 obs. of  16 variables:
 $ V1      : Factor w/ 2 levels "a","b": 2 1 1 2 2 2 2 1 2 2 ...
 $ V2      : num  30.8 58.7 24.5 27.8 20.2 ...
 $ V3      : num  0 4.46 0.5 1.54 5.62 ...
 $ V4      : Factor w/ 3 levels "l","u","y": 2 2 2 2 2 2 2 2 3 3 ...
 $ V5      : Factor w/ 3 levels "g","gg","p": 1 1 1 1 1 1 1 1 3 3 ...
 $ V6      : Factor w/ 14 levels "aa","c","cc",..: 13 11 11 13 13 10 12 3 9 13 ...
 $ V7      : Factor w/ 9 levels "bb","dd","ff",..: 8 4 4 8 8 8 4 8 4 8 ...
 $ V8      : num  1.25 3.04 1.5 3.75 1.71 ...
 $ V9      : Factor w/ 2 levels "f","t": 2 2 2 2 2 2 2 2 2 2 ...
 $ V10     : Factor w/ 2 levels "f","t": 2 2 1 2 1 1 1 1 1 1 ...
 $ V11     : int  1 6 0 5 0 0 0 0 0 0 ...
 $ V12     : Factor w/ 2 levels "f","t": 1 1 1 2 1 2 2 1 1 2 ...
 $ V13     : Factor w/ 3 levels "g","p","s": 1 1 1 1 3 1 1 1 1 1 ...
 $ V14     : int  202 43 280 100 120 360 164 80 180 52 ...
 $ V15     : int  0 560 824 3 0 0 31285 1349 314 1442 ...
 $ Response: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...

  0   1 
357 296 

aCRM documentation built on May 1, 2019, 8:29 p.m.