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In Data Mining and Predictive Analytics, the step-by-step hands-on solutions of real-world business problems using widely available data mining techniques applied to real-world datasets

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11.5 Decision Rules

11.6 Comparison of the C5.0 and CART Algorithms Applied to Real DataThe R Zone

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R References

Exercises

Chapter 22: Measuring Cluster Goodness

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32.2 Models that use Misclassification Costs

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Part 4: Summarization And Visualization Of Bivariate Relationships

Index

End User License Agreement

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Figure 3.7Figure 3.8Figure 3.9Figure 3.10Figure 3.11Figure 3.12Figure 3.13Figure 3.14Figure 3.15Figure 3.16Figure 3.17bFigure 3.18bFigure 3.19aFigure 3.20Figure 3.21Figure 3.22Figure 3.23Figure 3.24Figure 3.25Figure 3.26Figure 3.27Figure 3.28Figure 3.29Figure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.6Figure 4.7Figure 6.1Figure 6.2Figure 5.3Figure 5.4

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Figure 6.1Figure 6.2Figure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10Figure 8.12Figure 8.11Figure 8.13Figure 8.14Figure 8.15Figure 8.16Figure 8.17Figure 8.18Figure 8.19Figure 8.20Figure 8.21Figure 8.22Figure 8.23Figure 9.1Figure 9.2Figure 9.3Figure 9.4

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Figure 12.6Figure 12.7Figure 12.8Figure 12.9Figure 12.10Figure 13.1Figure 13.2Figure 13.3Figure 13.4Figure 13.5Figure 13.6Figure 13.7Figure 14.1Figure 14.2Figure 14.3Figure 14.4Figure 14.5Figure 14.6Figure 15.1Figure 15.2Figure 15.3Figure 15.4Figure 16.1a–cFigure 18.1Figure 18.2Figure 18.3Figure 18.4Figure 18.5Figure 18.6Figure 18.7Figure 18.8Figure 19.1Figure 19.2

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Figure 32.2Figure A.1Figure A.2Figure A.3Figure A.4Figure A.5Figure A.6Figure A.7Figure A.9Figure A.8

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Table 4.12Table 4.13Table 6.1Table 6.2Table 6.3Table 6.4Table 6.5Table 6.6Table 6.7Table 6.1Table 6.2Table 6.3Table 6.4Table 6.5Table 6.6Table 6.7Table 6.8Table 6.9Table 6.10Table 6.12Table 6.13Table 7.1Table 8.1Table 8.2Table 8.3Table 8.4Table 8.5Table 8.6Table 8.7Table 8.8Table 8.9Table 8.10Table 8.11

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Table 26.11Table 26.12Table 27.1Table 27.2Table 27.3Table 27.4Table 27.5Table 27.6Table 27.7Table 27.8Table 29.1Table 29.2Table 29.3Table 30.1Table 30.2Table 31.1Table 31.2Table 31.3Table 31.4Table 31.5Table 31.6Table 31.7Table 31.8Table 31.9Table 31.10Table 32.1Table 32.2Table 32.3Table 32.4Table 32.5Table A.1Table A.2Table A.3

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Table A.4Table A.5

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Second Edition

DANIEL T LAROSE

CHANTAL D LAROSE

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Published by John Wiley & Sons, Inc., Hoboken, New Jersey

Published simultaneously in Canada

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the

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To those who have gone before us, And to those who come after us,

In the Family Tree of Life…

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Preface

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According to the research firm MarketsandMarkets, the global big data market is expected togrow by 26% per year from 2013 to 2018, from $14.87 billion in 2013 to $46.34 billion in

2018.1 Corporations and institutions worldwide are learning to apply data mining and

predictive analytics, in order to increase profits Companies that do not apply these methodswill be left behind in the global competition of the twenty-first-century economy

Humans are inundated with data in most fields Unfortunately, most of this valuable data,which cost firms millions to collect and collate, are languishing in warehouses and

managers and analysts who know how to operate companies by using insights from bigdata… We project that demand for deep analytical positions in a big data world could

exceed the supply being produced on current trends by 140,000 to 190,000 positions …

In addition, we project a need for 1.5 million additional managers and analysts in the

United States who can ask the right questions and consume the results of the analysis ofbig data effectively

This book is an attempt to help alleviate this critical shortage of data analysts

Data mining is becoming more widespread every day, because it empowers companies touncover profitable patterns and trends from their existing databases Companies and

institutions have spent millions of dollars to collect gigabytes and terabytes of data, but arenot taking advantage of the valuable and actionable information hidden deep within theirdata repositories However, as the practice of data mining becomes more widespread,

companies that do not apply these techniques are in danger of falling behind, and losing

market share, because their competitors are applying data mining, and thereby gaining thecompetitive edge

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In Data Mining and Predictive Analytics, the step-by-step hands-on solutions of real-world

business problems using widely available data mining techniques applied to real-world datasets will appeal to managers, CIOs, CEOs, CFOs, data analysts, database analysts, and otherswho need to keep abreast of the latest methods for enhancing return on investment

Using Data Mining and Predictive Analytics, you will learn what types of analysis will

uncover the most profitable nuggets of knowledge from the data, while avoiding the potential

pitfalls that may cost your company millions of dollars You will learn data mining and

predictive analytics by doing data mining and predictive analytics.

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The growth of new off-the-shelf software platforms for performing data mining has kindled anew kind of danger The ease with which these applications can manipulate data, combinedwith the power of the formidable data mining algorithms embedded in the black-box

software, make their misuse proportionally more hazardous

In short, data mining is easy to do badly A little knowledge is especially dangerous when it

comes to applying powerful models based on huge data sets For example, analyses carriedout on unpreprocessed data can lead to erroneous conclusions, or inappropriate analysis may

be applied to data sets that call for a completely different approach, or models may be derivedthat are built on wholly unwarranted specious assumptions If deployed, these errors in

analysis can lead to very expensive failures Data Mining and Predictive Analytics will help

make you a savvy analyst, who will avoid these costly pitfalls

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Understanding the Underlying Algorithmic and Model Structures

The best way to avoid costly errors stemming from a blind black-box approach to data miningand predictive analytics is to instead apply a “white-box” methodology, which emphasizes anunderstanding of the algorithmic and statistical model structures underlying the software

Data Mining and Predictive Analytics applies this white-box approach by

clearly explaining why a particular method or algorithm is needed;

getting the reader acquainted with how a method or algorithm works, using a toy example (tiny data set), so that the reader may follow the logic step by step, and thus gain a white-

box insight into the inner workings of the method or algorithm;

providing an application of the method to a large, real-world data set;

using exercises to test the reader's level of understanding of the concepts and algorithms;providing an opportunity for the reader to experience doing some real data mining onlarge data sets

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Data Mining Methods and Models walks the reader through the operations and nuances of

the various algorithms, using small data sets, so that the reader gets a true appreciation ofwhat is really going on inside the algorithm For example, in Chapter 21, we follow step bystep as the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithmworks through a tiny data set, showing precisely how BIRCH chooses the optimal clusteringsolution for this data, from start to finish As far as we know, such a demonstration is unique

to this book for the BIRCH algorithm Also, in Chapter 27, we proceed step by step to find theoptimal solution using the selection, crossover, and mutation operators, using a tiny data set,

Chapter Exercises: Checking to Make Sure You Understand It

Data Mining and Predictive Analytics includes over 750 chapter exercises, which allow

readers to assess their depth of understanding of the material, as well as have a little fun

playing with numbers and data These include Clarifying the Concept exercises, which help to clarify some of the more challenging concepts in data mining, and Working with the Data

exercises, which challenge the reader to apply the particular data mining algorithm to a smalldata set, and, step by step, to arrive at a computationally sound solution For example, in

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Some readers may be a bit rusty on some statistical and graphical concepts, usually

encountered in an introductory statistics course Data Mining and Predictive Analytics

contains an appendix that provides a review of the most common concepts and terminologyhelpful for readers to hit the ground running in their understanding of the material in thisbook

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Data Mining and Predictive Analytics culminates in a detailed Case Study Here the reader

has the opportunity to see how everything he or she has learned is brought all together tocreate actionable and profitable solutions This detailed Case Study ranges over four chapters,and is as follows:

Chapter 29: Case Study, Part 1 : Business Understanding, Data Preparation, and EDA

Chapter 30: Case Study, Part 2 : Clustering and Principal Components Analysis

Chapter 31: Case Study, Part 3 : Modeling and Evaluation for Performance and

Interpretability

Chapter 32: Case Study, Part 4 : Modeling and Evaluation for High Performance Only

The Case Study includes dozens of pages of graphical, exploratory data analysis (EDA),

predictive modeling, customer profiling, and offers different solutions, depending on therequisites of the client The models are evaluated using a custom-built data-driven cost-

benefit table, reflecting the true costs of classification errors, rather than the usual methodssuch as overall error rate Thus, the analyst can compare models using the estimated profitper customer contacted, and can predict how much money the models will earn, based on thenumber of customers contacted

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