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Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R

Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R

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Description

  • Copyright 2014
  • Dimensions: 7" x 9-1/4"
  • Pages: 348
  • Edition: 1st
  • eBook (Watermarked)
  • ISBN-10: 0-13-341297-0
  • ISBN-13: 978-0-13-341297-0

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. 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 challenges and business cases, 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 even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

Sample Content

Table of Contents

Preface     v

Figures     ix

Tables     xiii

Exhibits     xv

1. Analytics and Data Science     1

2. Advertising and Promotion     15

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     113

9. Sports Analytics     149

10. Brand and Price     173

11. Spatial Data Analysis     209

12. The Big Little Data Game     231

A. There's a Pack for That     237

B. Measurement     253

C. Code and Utilities     267

Bibliography     297

Index     327

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