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Applying predictive analytics = find...

McCarthy, Richard V.

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Applying predictive analytics = finding value in data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applying predictive analytics/ by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci.
Reminder of title:	finding value in data /
Author:	McCarthy, Richard V.
other author:	McCarthy, Mary M.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xv, 274 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-83070-0
ISBN:	9783030830700

Applying predictive analytics = finding 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

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

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

562972
Data mining.

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

Dewey Class. No.: 006.312

Applying predictive analytics = finding value in data /
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245 1 0 $a Applying predictive analytics $h [electronic resource] : $b finding value in data / $c by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xv, 274 p. : $b ill., digital ; $c 24 cm.
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.
630 0 0 $a Enterprise miner $v Textbooks. $3 3592627
650 0 $a Data mining. $3 562972
650 0 $a SAS (Computer program language) $3 1234898
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Big Data/Analytics. $3 2186785
700 1 $a McCarthy, Mary M. $3 1279112
700 1 $a Ceccucci, Wendy. $3 3592626
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-83070-0
950 $a Engineering (SpringerNature-11647)