the second R edition of Machine Learning for Business Analytics. This edition also includes: * A new co-author, 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 * End-of-chapter 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 solutions This textbook is an ideal resource for upper-level 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.