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Categorical Variables Cumulative Frequency Cumulative Percent Cumulative Percent Cumulative Frequency Cumulative Percent Cumulative Frequency Cumulative Percent table continued on next p

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Page 338Extremes

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Page 339

Age of File

Univariate ProcedureVariable=AGE_FIL

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Page 340Quantiles (Def=5)

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Page 341

Number of 30 -Day Delinquencies

Univariate ProcedureVariable=NO30DAY

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Page 342Quantiles (Def=5)

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Page 343

Replacing Missing Values for Income

The first frequency shows the distribution of HOME EQUITY This is used to create the matrix that displays the mean INCOME by HOME EQUITY range and AGE Group

Cumulative Frequency

Cumulative Percent

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Mean INCOME by HOME EQUITY and AGE Group

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Page 345All variables left in the model are significant at the 0.1000 level.

OBS _MODEL_ _TYPE_ _DEPVAR_ _RMSE_ INTERCEP INFD_AG2

1 INC_REG PARMS INC_EST2 46.4383 36.8761 0.11446

OBS HOM_EQU2 CREDLIN2 TOT_BAL2 INC_EST2

1 -.0000034348 .00011957 -.0000067044 -1

The following print output displays the values for INCOME after regression substitution

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72540 42.5443

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Page 347

APPENDIX B—

UNIVARIATE ANALYSIS OF CATEGORICAL VARIABLES

In this appendix you will find simple frequencies of the categorical variables discussed in chapter 3

Categorical Variables

Cumulative Frequency

Cumulative Percent

Cumulative Percent

Cumulative Frequency

Cumulative Percent

Cumulative Frequency

Cumulative Percent

(table continued on next page)

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Cumulative Percent

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Cumulative Percent

Cumulative Percent

FINL_ID Frequency Percent

Cumulative Frequency

Cumulative Percent

Cumulative Percent

Cumulative Frequency

Cumulative Percent

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I 124038 17.0 417369 57.2

(table continued on next page)

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Cumulative Percent

Cumulative Frequency

Cumulative Percent

Cumulative Percent

Cumulative Percent

Cumulative Percent

Cumulative Percent

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Cumulative Percent

Cumulative Percent

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Page 352

(table continued from previous page)

Cumulative Frequency

Cumulative Percent

Cumulative Percent

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Page 353

(table continued from previous page)

Cumulative Frequency

Cumulative Percent

Cumulative Percent

Cumulative Frequency

Cumulative Percent

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V 8311 1.1 729228 100.0

(table continued on next page)

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Page 354

(table continued from previous page)

Cumulative Frequency

Cumulative Percent

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Page 355

RECOMMENDED READING

Berry, Michael J.A., and Gordon Linoff 1997 Data Mining Techniques New York: John Wiley & Sons.

Berry, Michael J.A., and Gordon Linoff 1997 Mastering Data Mining New York: John Wiley & Sons.

Hosmer, David W., and Stanley Lemeshow 1989 Applied Logistic Regression New York: John Wiley &

Sons

Hughes, Arthur M 1994 Strategic Database Marketing Chicago: Probus Publishing.

Journal of Targeting, Measurement and Analysis for Marketing London: Henry Stewart

Publications

Tukey, John W 1977 Exploratory Data Analysis Reading, MA: Addison-Wesley.

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Page 357

WHAT'S ON THE CD-ROM?

The CD -ROM contains step-by -step instructions for developing the data models described in Data Mining Cookbook

Written in SAS code, you can use the contents as a template to create your own models The content on the CD -ROM is equivalent to taking a three-day course in data modeling

Within chapters 3 through 12 of this book are blocks of SAS code used to develop, validate, and implement the data models By adapting this code and using some common sense, it is possible to build a model from the data preparation phase through model development and validation However, this could take a considerable amount of time and introduce the possibility of coding errors To simplify this task and make the code easily accessible for a variety of model types, a companion CD-ROM is available for purchase separately

The CD -ROM includes full examples of all the code necessary to develop a variety of models including response, approval, attrition or churn, risk, and lifetime or net present value Detailed code for developing the objective function includes examples from the credit cards, insurance, telecommunications, and catalog industries The code is well documented and explains the goals and methodology for each step The only software needed is BASE SAS and

SAS/STAT The spreadsheets used for creating gains tables and lift charts are also included These can be used by plugging in the preliminary results from the analyses created in SAS While the steps before and after the model

processing can be used in conjunction with any data modeling software package, the code can also serve as a stand-alone modeling template The model processing steps focus on variable preparation for use in logistic regression Additional efficiencies in the form of SAS macros for variable processing and validation are included

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Page 358

Hardware Requirements

To use this CD-ROM, your system must meet the following requirements:

Platform/processor/operating system: Windows® 95, NT 4.0 or higher; 200 MHz

Pentium

RAM: 64 MB minimum; 128 MB recommended

Hard drive space: Nothing will install to the hard drive, but in order to make a local copy of all the files on the

CD-ROM requires 5 MB of free space

Peripherals: CD-ROM drive You also will need to have the following applications to make full use of the CD-ROM: a

running copy of SAS 6.12 or higher software to process SAS code provided; a browser such as Internet Explorer or Netscape Navigator to navigate the CD-ROM; Microsoft Excel 97/2000 or Microsoft Excel 5.0/95

Installing the Software

Insert the CD-ROM and launch the readme.htm file in a web browser, or navigate using Windows Explorer to browse the contents of the CD The model programs and output are in text format that can be opened in any editing software (including SAS) that reads ASCII files Spreadsheets are in Microsoft Excel 97/2000 and Microsoft Excel 5.0/95 Launch the application (SAS 6.12 or higher) and open the file directly from the CD-ROM If you wish to make changes, you can rename the files and save them to your local hard drive

Using the Software

The CD is organized into folders that correspond to each chapter in Data Mining Cookbook Within each folder are

sub-folders containing SAS programs, SAS output, Excel spreadsheets The programs can be used as templates You just need to change the data set and variable names More specific instructions on how to use the programs are included The

output is included to provide a more complete understanding of the recipes in Data Mining Cookbook The spreadsheets contain all formulas used to create the tables and charts in Data Mining Cookbook.

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hiring and teamwork, 21–22

product focus versus customer focus, 22–23

American Standard for Computer Information Interchange See ASCII files

Analyst

hiring of, 21

retaining, 22

teamwork with, 22

Analytics See Adaptive company

Approval models See Risk models

See also Modeling churn

case example of, 42

credit cards and silent type of, 258

defining to optimize profits, 259–261

definition of, 11

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