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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
4.4 (20)
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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

4.4 (20)
By: Keith McCormick, Abbott

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (11 chapters)
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10
Index

Introduction

This is a special chapter because it does not consist of recipes but four essays through which it addresses the subtasks within the opening phase of any data mining project—the business understanding phase:

"Understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition".

Business understanding is about problem definition. It should involve management. It should involve stakeholders. It should involve key players who will benefit from it or will be involved in the deployed model; if deployment has not been discussed, that oversight needs to be addressed during this phase as well. "Why are we doing this?" is the question of this phase. It is about restating lofty goals, and turning vaguely-defined needs into a soluble problem. It is about making better decisions, decisions informed by data.

What decisions are you trying to make using data?

The business understanding...

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