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


As consumers, most of us have encountered recommendation engines where a movie is recommended based upon prior viewing habits or books are recommended based upon prior purchases (or even based simply on prior viewing behavior). Association Rules analysis is often described primarily as kind of market basket analysis, but while it is strongly associated with retail data it can be applied in other areas. For example, in the area of predictive maintenance, a pair of part failures might be frequently associated with a third part failure even though the third part has not yet shown evidence of trouble. Modeler supports three different Association nodes: Apriori, Carma, and Association Rules.

The fourth algorithm, Sequence, is a bit different. It takes either the time-date stamp, or a simple ranking in time, into account. The transactions in Association Rules often occur at the same time. For instance, on a receipt at a grocery store many items might be listed. Hot dogs and hot dog...