Data mining for business analytics : concepts, techniques, and applications in JMP Pro
By: Shmueli, Galit
Title By: Bruce, Peter C | 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: eBookItem 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 |
, Shelving location: Closed Reserve Close shelf browser
005.8 ST NE Network security essentials : | 005.8092 WE PE Penetration testing : | 006.312 AG DA Data mining : | 006.312 SH DA Data mining for business analytics : | 006.696 MU AU Autodesk Maya 2018 : | 006.696 MU AU Autodesk Maya 2019 : | 006.696 MU AU Autodesk Maya 2020 : |
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