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

Introduction to data mining

In this chapter, we will place IBM SPSS Modeler and its use in a broader context. Modeler was developed as a tool to perform data mining. Although the phrase predictive analytics is more common now, when Modeler was first developed in the 1990s, this type of analytics was almost universally called data mining. The use of the phrase data mining has evolved a bit since then to emphasize the exploratory aspect, especially in the context of big data and sometimes with a particular emphasis on the mining of private data that has been collected. This will not be our use of the term. Data mining can be defined in the following way:

Data mining is the search of data, accumulated during the normal course of doing business, in order to find and confirm the existence of previously unknown relationships that can produce positive and verifiable outcomes through the deployment of predictive models when applied to new data.

Several points are worth emphasizing:

  • The data is not new
  • The data that can solve the problem was not collected solely to perform data mining
  • The data miner is not testing known relationships (neither hypotheses nor hunches) against the data
  • The patterns must be verifiable
  • The resulting models must be capable of something useful
  • The resulting models must actually work when deployed on new data

In the late 1990s, a process was developed called the Cross Industry Standard Process for Data Mining (CRISP-DM). We will be drawing heavily from that tradition in this chapter, and CRISP-DM can be a powerful way to organize your work in Modeler. It is because of our use of this process in organizing this book's material that prompts us to use the term data mining. It is worth noting that the team that first developed Modeler, originally called Clementine, and the team that wrote CRISP-DM have some members in common.