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Data mining for business analytics : concepts, techniques, and applications in R / by Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl.

By: Shmueli, Galit,, 1971- author.
Contributor(s): Bruce, Peter C.,, 1953- author. | Yahav, Inbal, author. | Patel, Nitin R. (Nitin Ratilal), author. | Lichtendahl, Kenneth C.,, 1969- author.
Call number: HF5548.2 S55 2018 Material type: TextTextPublisher: Hoboken, New Jersey : John Wiley & Sons, 2018Description: xxix, 544 pages ; 26 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781118879368 (hbk.)Subject(s): Business -- Data processing | Data mining | R (Computer program language) | Business mathematics -- Computer programsDDC classification: 658.4/03802856312
Contents:
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 -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases.
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Item type Location Location Call number Copy number Barcode Status Date due
10000 General Book General Book ODI General Collection
ODI General Collection HF5548.2 S55 2018 (Browse shelf) 1 1000517269 Available

Includes bibliographical references and 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 -- Association rules and collaborative filtering -- Cluster analysis -- Handling time series -- Regression-based forecasting -- Smoothing methods -- Social network analytics -- Text mining -- Cases.

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