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Applying Predictive Analytics

Finding Value in Data

  • Richard V. McCarthy
  • Mary M. McCarthy
  • Wendy Ceccucci
  • Leila Halawi

Table of contents

  1. Front Matter
    Pages i-x
  2. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 1-25
  3. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 27-56
  4. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 57-87
  5. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 89-121
  6. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 123-144
  7. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 145-173
  8. Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
    Pages 175-199
  9. Back Matter
    Pages 201-205

About this book

Introduction

This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes.
  • Focuses on how to use predictive analytic techniques to analyze historical data for the purpose of predicting future results;
  • Takes an applied approach and focus on solving business problems using predictive analytics and features case studies and a variety of examples;
  • Uses examples in SAS Enterprise Miner, one of world’s leading analytics software tools.

Keywords

Predicative analytics SAS Enterprise Miner Neural Networks Machine Learning Supervised learning unsupervised learning Data mining Business analytics Decision trees Complex analytics model Using analytics models Building analytics models Real-life business analytics examples Applied data analytics textbook

Authors and affiliations

  • Richard V. McCarthy
    • 1
  • Mary M. McCarthy
    • 2
  • Wendy Ceccucci
    • 3
  • Leila Halawi
    • 4
  1. 1.Computer Information SystemsQuinnipiac UniversityHamdenUSA
  2. 2.Central Connecticut State UniversityNew BritainUSA
  3. 3.Quinnipiac UniversityHamdenUSA
  4. 4.Embry-Riddle Aeronautical UniversityClearwaterUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-14038-0
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-14037-3
  • Online ISBN 978-3-030-14038-0
  • Buy this book on publisher's site