Modeling techniques in predictive analytics with Python and R : a guide to data science

著者

    • Miller, Thomas W.

書誌事項

Modeling techniques in predictive analytics with Python and R : a guide to data science

Thomas W. Miller

Pearson Education, Inc., 2014, c2015

大学図書館所蔵 件 / 全2件

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注記

Includes bibliographical references (p. 379-412) and index

内容説明・目次

内容説明

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code-in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You'll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R-not complex math. Step by step, you'll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each 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. Thomas W. Miller, leader of Northwestern University's pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you're new to predictive analytics, you'll gain a strong foundation for achieving accurate, actionable results. If you're already working in the field, you'll master powerful new skills. If you're familiar with either Python or R, you'll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you're new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you're already a modeler, programmer, or manager, you'll learn crucial skills you don't already have. Using Python and R, Miller addresses multiple business 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 code that delivers actionable insights. You'll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to 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

目次

Preface v 1 Analytics and Data Science 1 2 Advertising and Promotion 16 3 Preference and Choice 33 4 Market Basket Analysis 43 5 Economic Data Analysis 61 6 Operations Management 81 7 Text Analytics 103 8 Sentiment Analysis 1 35 9 Sports Analytics 187 10 Spatial Data Analysis 211 11 Brand and Price 239 12 The Big Little Data Game 273 A Data Science Methods 277 A.1 Databases and Data Preparation 279 A.2 Classical and Bayesian Statistics 281 A.3 Regression and Classification 284 A.4 Machine Learning 289 A.5 Web and Social Network Analysis 291 A.6 Recommender Systems 293 A.7 Product Positioning 295 A.8 Market Segmentation 297 A.9 Site Selection 299 A.10 Financial Data Science 300 B Measurement 301 C Case Studies 315 C.1 Return of the Bobbleheads 315 C.2 DriveTime Sedans 316 C.3 Two Month's Salary 321 C.4 Wisconsin Dells 325 C.5 Computer Choice Study 330 D Code and Utilities 335 Bibliography 379 Index 413

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