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Applying predictive analyticsfinding value in data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applying predictive analyticsby Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci.
其他題名:	finding value in data /
作者:	McCarthy, Richard V.
其他作者:	McCarthy, Mary M.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xv, 274 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-030-83070-0
ISBN:	9783030830700$q(electronic bk.)

Applying predictive analyticsfinding value in data /
McCarthy, Richard V.

Applying predictive analyticsfinding value in data /[electronic resource] :by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci. - Second edition. - Cham :Springer International Publishing :2022. - xv, 274 p. :ill., digital ;24 cm.

The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus 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 examples of how business analytics have been used in various aspects of organizations to solve issues 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. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.

ISBN: 9783030830700$q(electronic bk.)

Standard No.: 10.1007/978-3-030-83070-0doiSubjects--Uniform Titles:

Enterprise miner
--Textbooks.Subjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / M33 2022

Dewey Class. No.: 006.312

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520 $a The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus 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 examples of how business analytics have been used in various aspects of organizations to solve issues 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. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project.
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