Book Image

IBM SPSS Modeler Cookbook

By : Keith McCormick, Abbott
Book Image

IBM SPSS Modeler Cookbook

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)
10
Index

Introduction

This chapter addresses the clean subtask of the data preparation phase. CRISP-DM describes this subtask in the following way:

Raise the data quality to the level required by the selected analysis techniques. This may involve selection of clean subsets of the data, the insertion of suitable defaults, or more ambitious techniques such as the estimation of missing data by modeling.

While this chapter can't tackle the entire subject of cleaning data, it addresses three themes, and all three themes involve working with data that is incomplete in some way:

  • Avoiding the missing data
  • Imputing the missing data
  • Fuzzy matching

The first two recipes address the first theme, that is, how to deal with missing data. Sometimes a null value indicates that a value is unknown, but very frequently a null value is the only appropriate value because for the particular case (customer) the value is non-applicable. In these instances imputation is usually not the best choice.

However, when the missing...