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Predictive Modeling and Analytics - Strickland, Jeffrey

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    Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9781312375444_dbm

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        Avis sur Predictive Modeling And Analytics de Strickland, Jeffrey Format Broché  - Livre Économie

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        Présentation Predictive Modeling And Analytics de Strickland, Jeffrey Format Broché

         - Livre Économie

        Livre Économie - Strickland, Jeffrey - 01/08/2014 - Broché - Langue : Anglais

        . .

      • Auteur(s) : Strickland, Jeffrey
      • Editeur : Lulu.Com
      • Langue : Anglais
      • Parution : 01/08/2014
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 488
      • Expédition : 785
      • Dimensions : 22.9 x 15.2 x 2.9
      • ISBN : 9781312375444



      • Résumé :
        This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no by-hand exercises)....

        Biographie:
        LinkedIn: https://www.linkedin.com/in/jeffreystrickland/ Related Books by the Author: Data Science Applications using R. Copyright ? 2017. Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-81042-0, Predictive Crime Analysis using R. Copyright? 2018, Jeffrey Strickland. Lulu, Inc. ISBN 978-0-359-43159-5, Logistic Regression - Inside-Out. Copyright ? 2017 by Jeffrey S. Strickland. Glasstree, Inc. ISBN 978-1-365-27041-3, Time Series Analysis using Open-Source Tools. Copyright? 2016, Jeffrey Strickland Glasstree, Inc. ISBN 978-1-5342-0100-2 , Predictive Analytics using R. Copyright ? 2016 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-84101-7, Data Analytics Using Open-Source Tools. Copyright ? 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-365-21384-7, Data Science and Analytics for Ordinary People. Copyright ? 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-28062-5, Operations Research using Open-Source Tools. Copyright ? 2015 by Jeffrey Strickland. Lulu Inc. ISBN 978-1-329-00404-7, Missile Flight Simulation - Surface-to-Air Missiles, 2nd Edition. Copyright ? 2015 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-329-64495-3, Verification and Validation for Modeling and Simulation Copyright ? 2014 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-312-74061-7, Mathematical Modeling of Warfare and Combat Phenomenon. Copyright ? 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-45839255-8 , Simulation Conceptual Modeling. Copyright ? 2011 by Jeffrey S. Strickland. Lulu.com. ISBN 978-1-105-18162-7, Discrete Event Simulation using ExtendSim 8. Copyright ? 2010 by Jeffrey S. Strickland. Lulu.com. ISBN 978-0-557-72821-3

        Sommaire:
        This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no by-hand exercises)....

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