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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Revised and Expanded Edition

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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Revised and Expanded Edition

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Description

  • Copyright 2015
  • Dimensions: 7" x 9-1/4"
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-388622-0
  • ISBN-13: 978-0-13-388622-1

To succeed with predictive analytics, you must understand it on three levels:

Strategy and management

Methods and models

Technology and code

This up-to-the-minute reference thoroughly covers all three categories.

Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have.

Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more.

Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value.

Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively.

All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller

If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike.

Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data.

You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights.

You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance.

This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods.

Gain powerful, actionable, profitable insights about:

  • Advertising and promotion
  • Consumer preference and choice
  • Market baskets and related purchases
  • Economic forecasting
  • Operations management
  • Unstructured text and language
  • Customer sentiment
  • Brand and price
  • Sports team performance
  • And much more

Sample Content

Table of Contents

Preface     v

1  Analytics and Data Science     1

2  Advertising and Promotion     14

3  Preference and Choice     29

4  Market Basket Analysis     37

5  Economic Data Analysis     53

6  Operations Management     67

7  Text Analytics     83

8  Sentiment Analysis     107

9  Sports Analytics     143

10  Spatial Data Analysis     167

11  Brand and Price     187

12  The Big Little Data Game     221

A  Data Science Methods     225

  A.1  Databases and Data Preparation     227

  A.2  Classical and Bayesian Statistics     229

  A.3  Regression and Classification     232

  A.4  Machine Learning     237

  A.5  Web and Social Network Analysis     239

  A.6  Recommender Systems     241

  A.7  Product Positioning     243

  A.8  Market Segmentation     245

  A.9  Site Selection     247

  A.10  Financial Data Science     248

B  Measurement     249

C  Case Studies     263

  C.1  Return of the Bobbleheads     263

  C.2  DriveTime Sedans     264

  C.3  Two Month’s Salary     269

  C.4  Wisconsin Dells     273

  C.5  Computer Choice Study     278

D  Code and Utilities     283

Bibliography     321

Index     355

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