Book Image

IBM SPSS Modeler Essentials

By : Jesus Salcedo, Keith McCormick
Book Image

IBM SPSS Modeler Essentials

By: Jesus Salcedo, Keith McCormick

Overview of this book

IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data. With almost 25 years of history, Modeler is the most established and comprehensive Data Mining workbench available. Since it is popular in corporate settings, widely available in university settings, and highly compatible with all the latest technologies, it is the perfect way to start your Data Science and Machine Learning journey. This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions. Using a single case study throughout, this intentionally short and focused book sticks to the essentials. The authors have drawn upon their decades of teaching thousands of new users, to choose those aspects of Modeler that you should learn first, so that you get off to a good start using proven best practices. This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. Assessing a model’s performance is as important as building it; this book will also show you how to do that. Finally, you will see how you can score new data and export your predictions. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
Table of Contents (19 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Data structure

Modeler can read data from a variety of different file formats. If you take a look at the Sources palette, you will see that Modeler can read data from any one of the data import nodes: text files, databases, IBM SPSS Statistics or Cognos, SAS, or Excel, in addition to many others. In fact, unlike programs such as Microsoft Excel or IBM SPSS Statistics, where you can manually enter data into the program, in Modeler you must read data into the software from an external file. In this chapter, the focus will be on reading data files from free-field text files, which is a common file type. We will also briefly show you the options for reading in data from two other commonly used sources, Microsoft Excel and ODBC databases.

The following figure depicts the typical data structure used in Modeler. In general, Modeler uses a data structure in which the rows of a table represent cases and the columns of a table represent variables. In this figure, we can see that each row represents...