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 4. Data Quality and Exploration

The previous chapter introduced the general data structure that is used in Modeler. You learned how to read and display data, and you were introduced to the concepts of the measurement level and the field roles. Now that you know how to bring data into Modeler, the next step is to assess the quality of the data. In this chapter you will:

  • Get an overview of the Data Audit node options
  • Go over the results of the Data Audit node
  • Be introduced to missing data
  • Discuss ways to address missing data

Once your data is in Modeler, you are ready to start exploring and become familiar with the characteristics of the data. You should review the distribution of each field so that you can become familiar with a dataset, but also so that you can identify potential problems that may arise. For continuous fields, you will want to inspect the range of values. For categorical fields, you will want to take a look at the number of distinct values. You will also have to consider...