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Data mining for business analytics : concepts, techniques, and applications in R / Galit Shmueli...[et al.].

By: Contributor(s): Material type: TextTextPublication details: Hoboken, NJ : John Wiley & Sons, ©2018.Description: xxix, 544 pages : illustrations ; 26 cmISBN:
  • 9781118879368
Subject(s): DDC classification:
  • 006.312  23 Sh558
Contents:
Part I: Preliminaries -- Part II: Data exploration and dimension reduction -- Part III: Performance evaluation -- Part IV: Prediction and classification methods -- Part V: Mining relationships among records -- Part VI: Forecasting time series -- Part VII: Data analytics -- Part VIII: Cases.
Summary: Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 006.312 Sh558 (Browse shelf(Opens below)) Available 138422
Total holds: 0

Includes bibliographical references and index.

Part I: Preliminaries --
Part II: Data exploration and dimension reduction --
Part III: Performance evaluation --
Part IV: Prediction and classification methods --
Part V: Mining relationships among records --
Part VI: Forecasting time series --
Part VII: Data analytics --
Part VIII: Cases.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

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