Predictive Analytics using R

552 Pages·2015·6.04 MB·English

by  Strickland Jeffrey

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Dr. Jeffrey Strickland is a Senior Predictive v
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This book is about predictive analytics. Yet, each chapter could
easily be handled by an entire volume of its own. So one might
think of this a survey of predictive modeling, both statistical
(parametric and nonparametric), as well as machine learning.
We define predictive model as a statistical model or machine
learning model used to predict future behavior based on past
behavior.
In order to use this book, one should have a basic
understanding of mathematical statistics (statistical inference,
models, tests, etc.) — this is an advanced book. Some
theoretical foundations are laid out (perhaps subtlety) but not
proven, but references are provided for additional coverage.
Every chapter culminates in an example using R. R is a free
software environment for statistical computing and graphics. It
compiles and runs on a wide variety of UNIX platforms,
Windows and MacOS. To download R, please choose your
preferred CRAN mirror at http://www.r-project.org/. An
introduction to R is also available at http://cran.r-project.org
/doc/manuals/r-release/R-intro.html.
The book is organized so that statistical models are presented
first (hopefully in a logical order), followed by machine learning
models, and then applications: uplift modeling and time series.
One could use this a textbook with problem solving in R—but
thereareno“by-hand”exercises.
J
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e Jeffrey Strickland
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ISBN 978-1-312-84101-790000 la
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ID:16206475
9 781312 841017
www.lulu.com

Predictive Analytics using R

By
Jeffrey S. Strickland
Simulation Educators
Colorado Springs, CO

Predictive Analytics using R

Copyright 2014 by Jeffrey S. Strickland. All rights Reserved
ISBN 978-1-312-84101-7
Published by Lulu, Inc. All Rights Reserved

Acknowledgements

The author would like to thank colleagues Adam Wright, Adam Miller,
Matt Santoni. Working with them over the past two years has validated
the concepts presented herein.
A special thanks to Dr. Bob Simmonds, who mentored me as a senior
operations research analyst.

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Contents

Acknowledgements ................................................................................. v
Contents ................................................................................................. vii
Preface ................................................................................................. xxv
More Books by the Author .................................................................xxvii
1. Predictive analytics ............................................................................ 1
Business Analytics ............................................................................. 1
Types ................................................................................................. 2
Descriptive analytics ................................................................... 2
Predictive analytics ..................................................................... 2
Prescriptive analytics .................................................................. 3
Applications ................................................................................ 3
Definition ........................................................................................... 3
Not Statistics...................................................................................... 4
Types ................................................................................................. 4
Predictive models ....................................................................... 5
Descriptive models ..................................................................... 6
Decision models .......................................................................... 6
Applications ....................................................................................... 6
Analytical customer relationship management (CRM) ............... 6
Clinical decision support systems ............................................... 7
Collection analytics ..................................................................... 7
Cross-sell ..................................................................................... 8
Customer retention .................................................................... 8
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Direct marketing ......................................................................... 8
Fraud detection .......................................................................... 9
Portfolio, product or economy-level prediction ....................... 10
Risk management ..................................................................... 10
Underwriting ............................................................................. 10
Technology and big data influences ................................................ 11
Analytical Techniques ...................................................................... 11
Regression techniques .............................................................. 12
Machine learning techniques ................................................... 19
Tools ................................................................................................ 22
Notable open source predictive analytic tools include: ........... 22
Notable commercial predictive analytic tools include: ............ 23
PMML ........................................................................................ 23
Criticism ........................................................................................... 24
2. Predictive modeling......................................................................... 25
Propensity Models .......................................................................... 25
Cluster Models ................................................................................ 28
Conclusion ....................................................................................... 30
Presenting and Using the Results of a Predictive Model ................ 31
Applications ..................................................................................... 32
Uplift Modeling ......................................................................... 32
Archaeology .............................................................................. 32
Customer relationship management ........................................ 33
Auto insurance .......................................................................... 33
Health care ................................................................................ 34
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