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Applying Predictive Analytics [electronic resource] : Finding Value in Data / by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi.

By: McCarthy, Richard V [author.].
Contributor(s): McCarthy, Mary M [author.] | Ceccucci, Wendy [author.] | Halawi, Leila [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: X, 205 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030140380.Subject(s): Telecommunication | Computational intelligence | Data mining | Quantitative research | Communications Engineering, Networks | Computational Intelligence | Data Mining and Knowledge Discovery | Data Analysis and Big DataAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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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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.

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