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McCormick - IBM SPSS Modeler cookbook

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McCormick IBM SPSS Modeler cookbook
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Over 60 practical recipes to achieve better results using the experts methods for data mining

Overview

  • Go beyond mere insight and build models than you can deploy in the day to day running of your business
  • Save time and effort while getting more value from your data than ever before
  • Loaded with detailed step-by-step examples that show you exactly how its done by the best in the business

In Detail

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.

What you will learn from this book

  • Use and understand the industry standard CRISP_DM process for data mining.
  • Assemble data simply, quickly, and correctly using the full power of extraction, transformation, and loading (ETL) tools.
  • Control the amount of time you spend organizing and formatting your data.
  • Develop predictive models that stand up to the demands of real-life applications.
  • Take your modeling to the next level beyond default settings and learn the tips that the experts use.
  • Learn why the best model is not always the most accurate one.
  • Master deployment techniques that put your discoveries to work making the most of your business most critical resources.
  • Challenge yourself with scripting for ultimate control and automation - its easier than you think!

Approach

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.

Who this book is for

If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

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IBM SPSS Modeler Cookbook

IBM SPSS Modeler Cookbook

Copyright 2013 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: October 2013

Production Reference: 1211013

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-84968-546-7

www.packtpub.com

Cover Image by Colin Shearer (<>)

Credits

Authors

Keith McCormick

Dean Abbott

Meta S. Brown

Tom Khabaza

Scott R. Mutchler

Reviewers

Matthew Brooks

Fabrice Leroy

Robert Nisbet

David Young Oh

Jesus Salcedo

Terry Taerum

Acquisition Editor

Edward Gordon

Lead Technical Editor

Arun Nadar

Copy Editor

Gladson Monteiro

Technical Editors

Tanvi Bhatt

Jalasha D'costa

Mrunmayee Patil

Shiny Poojary

Siddhi Rane

Project Coordinator

Shiksha Chaturvedi

Proofreader

Stephen Copestake

Indexer

Priya Subramani

Production Coordinator

Adonia Jones

Cover Work

Adonia Jones

Foreword

Our company, ISL was a provider of Artificial Intelligence tools and technology to organizations developing advanced software solutions. By 1992, what had started as a casual interest from our clients in applying some of our toolsthe machine learning modulesto their historic data had evolved into a promising practice in what was to become known as data mining. This was developing into a nice line of business for us, but was frustrating in a couple of ways:

First, we'd always intended that ISL should be a software supplier. Yet here we were, because of the complexity of the technologies involved, providing data mining on a consulting services basis.

Secondly, we were finding that data mining projects involved a lot of hard work, and that most of that work was boring. Unearthing significant patterns and delivering accurate predictionsthat part was fun. But most of our effort went on mundane tasks such as manipulating data into the formats required by the various modules and algorithms we applied.

So we built Clementineto make our job easier and allow us to focus on the interesting parts of projects, and to give us a tool we could provide to our clients. When the first prototypes were ready, we tested them by using them to re-run projects we'd previously executed manually. We found work that had previously taken several weeks was now reduced to under an hour; we'd obviously got something right.

As the embryonic data mining market grew, so did our business. We saw other vendors, with deeper pockets and vastly more resources than little ISL, introduce data mining tools, some of which tried to emulate the visual style of the Clementine's user interface. We were relieved when, as the inevitable shoot-outs took place, we found time and time again evaluators reporting that our product had a clear edge, both in terms of productivity and the problem-solving power it gave to analysts.

On reflection, the main reasons for our success were that we got a number of crucial things right:

Clementine's design and implementation, from the ground up, was object-oriented. Our visual programming model was consistent and "pure"; learn the basics, and everything is done in the same way.

We stuck to a guiding principle of, wherever possible, insulating the user from technology details. This didn't mean we made it for dummies; rather, we ensured that default configurations were as sensible as possible (and in places, truly smartwe weren't AI specialists for nothing), and that expert options such as advanced parameter settings were accessible without having to drop below the visual programming level.

We made an important design decision that predictive models should have the same status within the visual workflow as other tools, and that their outputs should be treated as first-order data. This sounds like a simple point, but the repercussions are enormous. Want more than the basic analysis of your model's performance? No problemrun its output through any of the tools in the workbench. Curious to know what might be going on inside your neural network? Use rule induction to tell you how combinations of inputs map onto output values. Want to have multiple models vote? Easy. Want to combine them in more complex ways? Just feed their inputs, along with any data you like, into a supermodel that can decide how best to combine their predictions.

The first two give productivity, plus the ability to raise your eyes from the technical details, think about the process of analysis at a higher level, and stay focused on each project's business objectives. Add the third, and you can experiment with novel and creative approaches that previously just weren't feasible to attempt.

So, 20 years on, what do I feel about Clementine/Modeler? A certain pride, of course, that the product our small team built remains a market leader. But mainly, over the years, awe at what I've seen people achieve with it: not just organizations who have made millions (sometimes, even billions) in returns from their data mining projects, but those who've done things that genuinely make the world a better place; from hospitals and medical researchers discovering new ways to diagnose and treat pediatric cancer, to police forces dynamically anticipating levels of crime risk around their cities and deploying their forces accordingly, with the deterrent effect reducing rates of murder and violent crime by tens of percent. And also, a humble appreciation for what I've learned over the years from users who took what we'd createda workbench and set of toolsand developed, refined, and applied powerful approaches and techniques we'd never thought of.

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.

When Keith McCormick approached me about writing this foreword, he suggested I might like to take a "then" and "now" perspective. This is certainly an interesting "now" in our industry. The advent of Big Datahuge volumes of data, of many varieties and varying veracity, available to support decision making at high velocitypresents unprecedented opportunities for organizations to use predictive analytics to gain value. There is a danger, though, that some of the hype around this will confuse potential adopters and confound their efforts to derive value for their business. One common misconception is that you just get all data you can together, and then poke around in the hope of finding something valuable. This approachtell me something interesting in this datawas what we always considered "the data mining question from hell", and is very unlikely to result in real, quantifiable benefit. Data mining is first and foremost a business activity, and needs to be focused on clear business objectives and goals, hence the crucial business understanding phase in CRISP-DM that starts every data mining project.

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