Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

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 (17 chapters)
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Using a full data model/partial data model approach to address missing data


It is common in data mining to have one category of customers more prone to having missing data. In fact, there may be a category of customers that are assured to have certain data missing. For instance, let's say that you have found in running your cell phone business that calculating the distance in time between phone upgrades is useful in estimating when the customer's next phone upgrade will be. A newly acquired customer will not have any prior phone history in the data set, but it would be risky to assume that your established customers are the same as your new customers.

How then to estimate the value of average months between new phones? One approach is to simply avoid the problem, and build a different model for your new customers and your established customers. In this recipe, we will learn how to diagnose the pattern of missing data and determine if this technique applies.

Getting ready

We will start with...