Salcedo J. IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions

Packt Publishing, 2017. — 238 p. — ISBN: 978-1788291118.Get to grips with the fundamentals of data mining and predictive analytics with IBM SPSS ModelerKey FeaturesGet up–and-running with IBM SPSS Modeler without going into too much depth.
Identify interesting relationships within your data and build effective data mining and predictive analytics solutions
A quick, easy–to-follow guide to give you a fundamental understanding of SPSS Modeler, written by the best in the businessBook DescriptionIBM 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.What you will learnUnderstand 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 predictionsWho This Book Is ForThis book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book.What this book covers
What you need for this bookWho this book is forConventions Reader feedback
Customer support
Introduction to Data Mining and Predictive Analytics
Introduction to data mining
CRISP-DM overview
The data mining process (as a case study)The Basics of Using IBM SPSS Modeler
Introducing the Modeler graphic user interface
Building streams
Modeler stream rules
Help optionsImporting Data into Modeler
Data structure
Levels of measurement and rolesData Quality and Exploration
Data Audit node optionsCleaning and Selecting Data
Selecting cases
Sorting cases
Identifying and removing duplicate cases
Reclassifying categorical valuesCombining Data Files
Combining data files with the Append node
Removing fields with the Filter node
Combining data files with the Merge nodeDeriving New Fields
Derive – Formula
Derive – Flag
Derive – Nominal
Derive – ConditionalLooking for Relationships Between Fields
Relationships between categorical fields
Relationships between categorical and continuous fields
Relationships between continuous fieldsIntroduction to Modeling Options in IBM SPSS Modeler
Classification
Association
SegmentationDecision Tree Models
Decision tree theory
CHAID theory
CHAID resultsModel Assessment and Scoring
Contrasting model assessment with the Evaluation phase