Machine Learning for Business Analytics

Peter C. Bruce , Galit Shmueli , Peter Gedeck , Inbal Yahav , Nitin R. Patel 지음

Wiley 2023년 02월 13일 출간

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MACHINE LEARNING FOR BUSINESS ANALYTICS Machine 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 best-selling 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 hands-on exercises and real-life 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 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 i

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Foreword by Ravi Bapna xxi Foreword by Gareth James xxiii Preface to the Second R Edition xxv Acknowledgments xxix PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What Is Business Analytics? 3 1.2 What Is Machine Learning? 5 1.3 Machine Learning, AI, and Related Terms 5 1.4 Big Data 7 1.5 Data Science 8 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 13 CHAPTER 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 Classification 18 Prediction 18 Association Rules and Recommendation Systems 18 Predictive Analytics 19 Data Reduction and Dimension Reduction 19 Data Exploration and Visualization 19 Supervised and Unsupervised Learning 20 2.3 The Steps in a Machine Learning Project 21 2.4 Preliminary Steps 23 Organization of Data 23 Predicting Home Values in the West Roxbury Neighborhood 23 vii viii CONTENTS Loading and Looking at the Data in R 24 Sampling from a Database 26 Oversampling Rare Events in Classification Tasks 27 Preprocessing and Cleaning the Data 28 2.5 Predictive Power and Overfitting 35 Overfitting 36 Creating and Using Data Partitions 38 2.6 Building a Predictive Model 41 Modeling Process 41 2.7 Using R for Machine Learning on a Local Machine 46 2.8 Automating Machine Learning Solutions 47 Predicting Power Generator Failure 48 Uber’s Michelangelo 50 2.9 Ethical Practice in Machine Learning 52 Machine Learning Software: The State of the Market (by Herb Edelstein) 53 Problems 57 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 63 3.1 Uses of Data Visualization 6

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작가정보

저자(글) Peter C. Bruce

저자(글) Galit Shmueli

저자(글) Peter Gedeck

저자(글) Inbal Yahav

저자(글) Nitin R. Patel