Modeling techniques in predictive analytics : business problems and solutions with R

著者

    • Miller, Thomas W.

書誌事項

Modeling techniques in predictive analytics : business problems and solutions with R

Thomas W. Miller

Pearson Education, Inc., c2014

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注記

Description based on 2nd printing, 2013

Includes bibliographical references (p. 297-325) and index

内容説明・目次

内容説明

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).

目次

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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