IBM SPSS Modeler Essentials

Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler
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IBM SPSS Modeler Essentials

Jesus Salcedo, Keith McCormick

Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS Modeler
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Book Details

ISBN 139781788291118
Paperback238 pages

Book Description

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

Chapter 1: Introduction to Data Mining and Predictive Analytics
Introduction to data mining
CRISP-DM overview
The data mining process (as a case study)
Summary
Chapter 2: The Basics of Using IBM SPSS Modeler
Introducing the Modeler graphic user interface
Building streams
Modeler stream rules
Help options
Summary
Chapter 3: Importing Data into Modeler
Data structure
Levels of measurement and roles
Summary
Chapter 4: Data Quality and Exploration
Data Audit node options
Summary
Chapter 5: Cleaning and Selecting Data
Selecting cases
Sorting cases
Identifying and removing duplicate cases
Reclassifying categorical values
Summary
Chapter 6: Combining Data Files
Combining data files with the Append node
Removing fields with the Filter node
Combining data files with the Merge node
Summary
Chapter 7: Deriving New Fields
Derive – Formula
Derive – Flag
Derive – Nominal
Derive – Conditional
Summary
Chapter 8: Looking for Relationships Between Fields
Relationships between categorical fields
Relationships between categorical and continuous fields
Relationships between continuous fields
Summary
Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler
Classification
Association
Segmentation
Summary
Chapter 10: Decision Tree Models
Decision tree theory
CHAID theory
CHAID results
Chapter 11: Model Assessment and Scoring
Contrasting model assessment with the Evaluation phase
Summary

What You Will Learn

  • Understand the basics of data mining and familiarize yourself with Modeler’s visual programming interface
  • Import data into Modeler and learn how to properly declare metadata
  • Obtain summary statistics and audit the quality of your data
  • Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields
  • Assess simple relationships using various statistical and graphing techniques
  • Get an overview of the different types of models available in Modeler
  • Build a decision tree model and assess its results
  • Score new data and export predictions

Authors

Table of Contents

Chapter 1: Introduction to Data Mining and Predictive Analytics
Introduction to data mining
CRISP-DM overview
The data mining process (as a case study)
Summary
Chapter 2: The Basics of Using IBM SPSS Modeler
Introducing the Modeler graphic user interface
Building streams
Modeler stream rules
Help options
Summary
Chapter 3: Importing Data into Modeler
Data structure
Levels of measurement and roles
Summary
Chapter 4: Data Quality and Exploration
Data Audit node options
Summary
Chapter 5: Cleaning and Selecting Data
Selecting cases
Sorting cases
Identifying and removing duplicate cases
Reclassifying categorical values
Summary
Chapter 6: Combining Data Files
Combining data files with the Append node
Removing fields with the Filter node
Combining data files with the Merge node
Summary
Chapter 7: Deriving New Fields
Derive – Formula
Derive – Flag
Derive – Nominal
Derive – Conditional
Summary
Chapter 8: Looking for Relationships Between Fields
Relationships between categorical fields
Relationships between categorical and continuous fields
Relationships between continuous fields
Summary
Chapter 9: Introduction to Modeling Options in IBM SPSS Modeler
Classification
Association
Segmentation
Summary
Chapter 10: Decision Tree Models
Decision tree theory
CHAID theory
CHAID results
Chapter 11: Model Assessment and Scoring
Contrasting model assessment with the Evaluation phase
Summary

Book Details

ISBN 139781788291118
Paperback238 pages
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