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Data mining and business analytics with R / Johannes Ledolter.

By: Ledolter, Johannes.
Material type: materialTypeLabelBookCall no.: CPSC QA 2013 620023Publication: Hoboken, N.J. : John Wiley & Sons, c2013Description: xi, 351 p. : ill. (some col.) ; 25 cm.ISBN: 9781118447147 (cloth); 111844714X (cloth).Subject(s): Data mining | R (Computer program language) | Commercial statistics
Contents:
Introduction -- Processing the information and getting to know your data -- Standard linear regression -- Local polynomial regression: a nonparametric regression approach -- Importance of parsimony in statistical modeling -- Penalty-based variable selection in regression models with many parameters (LASSO) -- Logistic regression -- Binary classification, probabilities, and evaluating classification performance -- Classification using a nearest neighbor analysis --The Naive Bayesian analysis: a model predicting a categorical response from mostly categorical predictor variables -- Multinomial logistic regression -- More on classification and a discussion on discriminant analysis -- Decision trees -- Further discussion on regression and classification trees, computer software, and other useful classification methods -- Clustering -- Market basket analysis: association rules and lift -- Dimension reduction: factor models and principal components -- Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares -- Text as data: text mining and sentiment analysis -- Network data -- Appendices: A. Exercises -- B. References.
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Item type Current location Collection Call number Status Date due Barcode Item holds Reading lists
Book Book Puey Ungphakorn Library, Rangsit Campus
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General Books CPSC QA 2013 620023 (See Similar Items) Available 31379014134499

คป.348 ภาคการศึกษาที่ 1

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Includes bibliographical references and index.

Introduction -- Processing the information and getting to know your data -- Standard linear regression -- Local polynomial regression: a nonparametric regression approach -- Importance of parsimony in statistical modeling -- Penalty-based variable selection in regression models with many parameters (LASSO) -- Logistic regression -- Binary classification, probabilities, and evaluating classification performance -- Classification using a nearest neighbor analysis --The Naive Bayesian analysis: a model predicting a categorical response from mostly categorical predictor variables -- Multinomial logistic regression -- More on classification and a discussion on discriminant analysis -- Decision trees -- Further discussion on regression and classification trees, computer software, and other useful classification methods -- Clustering -- Market basket analysis: association rules and lift -- Dimension reduction: factor models and principal components -- Reducing the dimension in regressions with multicollinear inputs: principal components regression and partial least squares -- Text as data: text mining and sentiment analysis -- Network data -- Appendices: A. Exercises -- B. References.

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