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

Combining data files with the Append node

Similar pieces of information for different groups of records may be stored in different data files. There is often a need to collectively analyze such data, possibly to compare performance over subsequent years or to discover group differences. To analyze such information within Modeler, the data files must be combined into one single file. The Append node joins two or more data sources together so that information held for different groups of records can be analyzed and compared. The following diagram depicts how the Append node can be used to combine two separate data files that contain similar information:

Let's go through an example of how to use the Append node to combine data files:

  1. Open the Append stream.
  2. Run a Table node from each source node so that you are familiar with each data file (not shown).

Notice the number of cases in each file. Also notice that one file has financial data for 1994 while the second file has financial data for 1995...