Foundations of Predictive Analytics

James Wu, Stephen Coggeshall

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February 15, 2012 by Chapman and Hall/CRC
Professional - 337 Pages - 14 B/W Illustrations
ISBN 9781439869468 - CAT# K13186
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Contains all of the key elements required for statistical modeling and predictive analytics
  • Covers a wide range of important but difficult-to-find topics
  • Gives a step-by-step mathematical derivation of each technique, from the underlying assumptions to final conclusion
  • Discusses the practical aspects of modeling and predicting, with many examples from consumer behavior modeling and more
  • Provides software and examples at www.DataMinerXL.com

Summary

Drawing on the authors’ two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts.

The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish–Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naïve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster–Shafer theory.

An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.

Web Resource
The book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.