Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics)

Download Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (FT Press Analytics) PDF EPUB

Grasp predictive analytics, from begin to surface finish

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Focus on strategy and administration

Master strategies and build versions

Transform your versions into highly-effective code—in both Python and R

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This one-of-a-kind book can help you make use of predictive analytics, Python, and R to resolve real business complications and drive actual competitive advantage. Miller, innovator of Northwestern University’s pioneering system in predictive analytics, addresses all you need to achieve success: strategy and management, strategies and versions, and technology and code.

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Step-by-step, you’ll walk through defining complications, determining data, crafting and optimizing versions, composing effective Python and R code, interpreting outcomes, and even more.

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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 recognized.

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Thomas W. You’ll expert predictive analytics through reasonable case research, intuitive data visualizations, and up-to-time code for both Python and R—not complicated mathematics.

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If you’re not used to predictive analytics, you’ll gain a solid foundation for attaining accurate, actionable outcomes. If you’re already employed in the field, you’ll master powerful new abilities. If you’re acquainted with either Python or R, you’ll learn how these languages complement one another, helping you to do a lot more. Miller manuals you through conceptually modeling each data established with words and numbers;ftpress.com/miller/

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Python and R give immense power in predictive analytics, data technology, and big data. Miller’s unique balanced strategy combines business context and quantitative equipment, illuminating each technique with thoroughly described code for the most recent variations of Python and R. Appendices include five full case studies, and an in depth primer on contemporary data science strategies. This book can help you leverage that capacity to solve actual business problems, and get real competitive benefit. If you’re not used to predictive analytics, Miller offers you a strong basis for attaining accurate, actionable results. and modeling it once again with reasonable code that delivers actionable insights.

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Using Python and R, Miller addresses multiple business issues, which includes segmentation, brand positioning, product choice modeling, prices research, finance, sports, textual content analytics, sentiment evaluation, and social network evaluation. He illuminates the usage of cross-sectional data, period series, spatial, and spatio-temporal data. Each chapter targets one of today’s important applications for predictive analytics, delivering skills and understanding to place models to function—and maximize their worth.

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All data sets, comprehensive Python and R code, and extra examples designed for download at http://www. If you’re currently a modeler, programmer, or supervisor, you’ll learn crucial abilities you don’t curently have.