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

Chapter 8. Looking for Relationships Between Fields

The previous chapters have focused on data preparation. You learned how to select and sort cases, how to identify and remove duplicate cases, how to reclassify and filter fields, how to combine different types of data files, and how to derive additional fields. Now that the dataset has been prepared, in this chapter you will learn how to assess the following:

  • Relationships between categorical variables
  • Relationships between categorical and continuous variables
  • Relationships between continuous variables

One major aspect of a data mining project is the development of a model; however, investigating simple relationships among variables can be extremely important in answering questions about what motivated a project. You may find, for example, that certain customer behaviors are associated with weather events, or purchasing specific products is associated with certain demographic characteristics. Although these patterns are not substitutes for...