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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Revised and Expanded Edition
- By Thomas W. Miller
- Published Sep 17, 2014 by Pearson FT Press. Part of the FT Press Analytics series.
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Description
- Copyright 2015
- Dimensions: 7" x 9-1/4"
- Edition: 1st
- eBook
- ISBN-10: 0-13-388618-2
- ISBN-13: 978-0-13-388618-4
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
More Information
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