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Applying predictive analyticsfinding...

McCarthy, Richard V.

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 ... [et al.].
其他題名:	finding value in data /
其他作者:	McCarthy, Richard V.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	x, 205 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
電子資源:	https://doi.org/10.1007/978-3-030-14038-0
ISBN:	9783030140380$q(electronic bk.)

Applying predictive analyticsfinding value in data /
Applying predictive analyticsfinding value in data /[electronic resource] :by Richard V. McCarthy ... [et al.]. - Cham :Springer International Publishing :2019. - x, 205 p. :ill., digital ;24 cm.

Introduction to Predictive Analytics -- Know Your Data - Data Preparation -- What do Descriptive Statistics Tell Us -- The First of the Big Three - Regression -- The Second of the Big Three - Decision Trees -- The Third of the Big Three - Neural Networks -- Model Comparisons and Scoring -- Appendix A -- Data Dictionary for the Automobile Insurance Claim Fraud Data Example -- Conclusion.

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.

ISBN: 9783030140380$q(electronic bk.)

Standard No.: 10.1007/978-3-030-14038-0doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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505 0 $a Introduction to Predictive Analytics -- Know Your Data - Data Preparation -- What do Descriptive Statistics Tell Us -- The First of the Big Three - Regression -- The Second of the Big Three - Decision Trees -- The Third of the Big Three - Neural Networks -- Model Comparisons and Scoring -- Appendix A -- Data Dictionary for the Automobile Insurance Claim Fraud Data Example -- Conclusion.
520 $a 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.
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