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 5. Cleaning and Selecting Data

The previous chapter focused on the data understanding phase of data mining. We spent some time exploring our data and assessing its quality. The previous chapter also mentioned that the Data Audit node is usually the first node that is used to explore and assess data. You were introduced to this node's options and learned how to look over its results. You were also introduced to the concept of missing data and shown ways to address it. In this chapter, you will learn how to:

  • Select cases
  • Sort cases
  • Identify and remove duplicate cases
  • Reclassify categorical values

Having finished the initial data understanding phase, we are ready to move onto the data preparation phase. Data preparation is the most time-consuming aspect of data mining. In fact, even in this very brief introductory book, we are devoting several chapters to this topic because it will be an integral part of every data mining project. However, every data mining project will require different...