Download Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R, Revised and Expanded Edition (FT Press Analytics) PDF EPUB
Author: Author
Pages: 384
Size: 3.756,76 Kb
Publication Date: October 11,2014
Category: Production, Operation & Management
To achieve success with predictive analytics, you need to understand it on three amounts:
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Strategy and administration
Methods and versions
Technology and code
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This up-to-the-minute reference completely covers all three types. Thomas W. If you’re not used to the discipline, it’ll provide you with the strong foundation you should obtain accurate, actionable outcomes. Throughout, Miller employs data visualization and statistical images to assist you explore data, present versions, and evaluate overall performance.
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Unlike competitive books, this information illuminates the self-discipline through reasonable vignettes and intuitive data visualizations– not really complex mathematics.
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Every chapter targets one of today’s crucial applications for predictive analytics, delivering skills and understanding to place models to function–and maximize their worth. Miller, innovator of Northwestern University’s pioneering plan in predictive analytics, manuals you through defining complications, determining data, crafting and optimizing versions, composing effective R code, interpreting results, and even more. Thomas W. He illuminates the usage of cross-sectional data, period series, spatial, and spatio-temporal data.com/miller
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If you would like to make the the majority of predictive analytics, data technology, and big data, this is actually the book for you personally.ftpress.
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All data sets, comprehensive R code, and extra examples designed for download at http://www.
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Now completely updated, this uniquely available book can help you make use of predictive analytics to resolve real business complications and drive actual competitive advantage. Miller’s exclusive balanced strategy combines business context and quantitative equipment, attractive to managers, analysts, programmers, and students as well.
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Miller addresses multiple business instances and challenges, which includes segmentation, brand positioning, product choice modeling, prices research, finance, sports, textual content analytics, sentiment evaluation, and social network evaluation.
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Reflecting extensive college student and instructor responses, this edition provides five classroom-examined case studies, improvements all code for fresh versions of R, clarifies code behavior even more clearly and totally, and covers contemporary data science methods a lot more efficiently.
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You’ll find out why each problem issues, what data are relevant, and how exactly to explore the info you’ve identified. Miller manuals you through conceptually modeling each data established with phrases and figures; and modeling it once again with realistic R applications that deliver actionable insights.
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You’ll walk through model construction, explanatory adjustable subset selection, and validation, mastering guidelines for improving out-of-sample predictive efficiency. If you’re currently a modeler, programmer, or manager, it’ll teach you crucial abilities you don’t yet have got.
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This edition provides five fresh case studies, improvements all code for the most recent variations of R, adds even more commenting to clarify the way the code works, and will be offering a more comprehensive and up-to-day primer on data technology strategies.
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Gain effective, actionable, rewarding insights about:
- Marketing and advertising
- Consumer choice and choice
- Marketplace baskets and related buys
- Economic forecasting
- Operations administration
- Unstructured text and vocabulary
- Client sentiment
- Brand and cost
- Sports team functionality
- Plus much more
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See also
- Sports Analytics and Data Science: Winning the Game with Methods and Models (FT Press Analytics)
- Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
- Business Analytics Principles, Concepts, and Applications with SAS: What, Why, and How (FT Press Analytics)