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

Identifying and removing duplicate cases

Datasets may contain duplicate records that often must be removed before data mining can begin. For example, the same individual may appear multiple times in a dataset with different addresses. The Distinct node finds or removes duplicate records in a dataset. The Distinct node, located in the Record Ops palette, checks for duplicate records and identifies the cases that appear more than once in a file so they can be reviewed and/or removed.

A duplicate case is defined by having identical data values on one or more fields that are specified. Any number or combination of fields may be used to specify a duplicate:

  1. Place a Distinct node from the Record Ops palette onto the canvas.
  2. Connect the Sort node to the Distinct node.
  3. Edit the Distinct node.

The Distinct node can be a bit tricky to use; this is why we will run this node a couple of times, and hopefully in this way its options will become well-defined. The Mode option controls how the Distinct node is...