Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (17 chapters)
IBM SPSS Modeler Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


The objective of data mining is to understand and predict behavior. A retailer wants to know why people buy, and how to sell more. An educator wants to know what factors influence educational and professional success and how to help students learn and prepare for a career. A criminologist wants to understand the factors that lead to crime, and how to prevent crime.

Data miners often speak of valuable patterns in data, and powerful models. What makes a pattern valuable? It's valuable if it adds to our understanding of behavior. What makes a model useful? It's useful if it is effective at predicting behavior.

Data miners aim to identify influential factors that drive behavior. When we identify those driving factors, through exploration of patterns in the data, we can understand behavior. If we can describe, with a quantitative model, the relationship between driving factors and behavior, then we can predict behavior.

It's easy for a data miner to make a model; that takes little more...