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IBM SPSS Modeler Cookbook

You're reading from   IBM SPSS Modeler Cookbook If you've already had some experience with IBM SPSS Modeler this cookbook will help you delve deeper and exploit the incredible potential of this data mining workbench. The recipes come from some of the best brains in the business.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781849685467
Length 382 pages
Edition 1st Edition
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Table of Contents (11) Chapters Close

Preface 1. Data Understanding FREE CHAPTER 2. Data Preparation – Select 3. Data Preparation – Clean 4. Data Preparation – Construct 5. Data Preparation – Integrate and Format 6. Selecting and Building a Model 7. Modeling – Assessment, Evaluation, Deployment, and Monitoring 8. CLEM Scripting A. Business Understanding Index

Who this book is for

This book envisions that you are a regular user of IBM SPSS Modeler, albeit perhaps on your first serious project. It assumes that you have taken an introductory course or have equivalent preparation. IBM's Modeler certification would be some indication of this, but the certification focuses on software operations alone and does not address the general data mining theory. Some familiarity with that would be of considerable assistance in putting these recipes into context. All the readers would benefit from a careful review of the CRISP-DM document, which is readily available on the Internet.

This book also assumes that you are using IBM SPSS Modeler for data mining and are interested in all of the software-related phases of CRISP-DM. This premise might seem strange, but since Modeler combines powerful ETL capability with advanced modeling algorithms, it is true that some Modeler uses the software primarily for ETL capabilities alone. This book roughly spends equal time on both. One of the advantages of the cookbook format, however, is that the reader is invited to skip around, reading out of order, reading some chapters and not others, reading only some of the recipes within chapters, gleaning only what is needed at the moment.

It does not assume that the reader possesses knowledge of SQL. Such knowledge will not be emphasized as Modeler considerably reduces the need for knowing SQL, although many data miners have this skill. This book does not assume knowledge of statistical theory. Such knowledge is always useful to the data miner, but the recipes in this book neither require this knowledge nor does the book assume prior knowledge of data mining algorithms. The recipes simply do not dive deep enough into this aspect of the topic to require it.

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