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Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R
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MACHINE LEARNING FOR BUSINESS ANALYTICSMachine learning also known as data mining or data analytics is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This bestselling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with handson exercises and reallife case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new coauthor, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques Endofchapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutionsThis textbook is an ideal resource for upperlevel undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
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