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

Imputing in-stream mean or median


Filling missing values with the mean or median is a common approach to removing missing values. Modeler has mechanisms for computing and filling missing values using either the Set Globals node or the Data Audit node. Unfortunately, both of these are terminal nodes and therefore require the user to run them as a separate step or as a script. Moreover, the options for which values to impute with are limited to the mean, mid-point, or (in the case of the Data Audit node) a constant.

In this recipe we will impute missing values with the median of a variable in-stream, without the use of @GLOBAL variables.

Getting ready

This recipe uses the following files:

  • Datafile: cup98lrn_reduced_vars3.sav

  • Stream file: Recipe - impute missing with fixed value.str

How to do it...

To impute missing values with the median of a variable:

  1. Open the stream (Recipe - impute missing with fixed value.str) by going to File | Open Stream.

  2. Make sure the datafile points to the correct path to...