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Data mining for business analytics : concepts, techniques, and applications in JMP Pro

By: Shmueli, Galit, 1971-
Title By: Bruce, Peter C, 1953- | Stephens, Mia L | Patel, Nitin R
Material type: BookPublisher: Hoboken, N.J. : John Wiley & Sons, c2017.Description: xxii, 442 p. : ill. ; 26 cm.ISBN: 9781118877432Program: MBAS904Subject(s): Business mathematics -- Computer programs | Business -- Data processing | Data mining | Computers -- generalDDC classification: 006.3/12 Online resources: eBook
Summary:
Featuring hands–on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real–world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.
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Item type Home library Call number url Status Date due Barcode Item holds Course reserves
CRS University of Wollongong in Dubai
Closed Reserve
006.312 SH DA (Browse shelf) link Available T0011189

MBAS902 Autumn2021

MBAS904 Autumn2021

Total holds: 0

Includes index.

Overview of the data mining process -- Data visualization -- Dimension reduction -- Evaluating predictive performance -- Multiple linear regression -- K-nearest neighbors (kNN) -- The naive Bayes classifier -- Classification and regression trees -- Logistic regression -- Neural nets -- Discriminant analysis -- Combining methods : ensembles and uplift modeling -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Cases.

Featuring hands–on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real–world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.

MBAS904

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