MACHINE LEARNING FOR BUSINESS ANALYTICS

Machine learning--also known as data mining or predictive 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 with Analytic Solver(r) Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting 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 fourth edition of Machine Learning for Business Analytics also includes:

• An expanded chapter on deep learning
• 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.

  Autorentext

  Galit Shmueli, PhD, is Distinguished Professor and Institute Director at National Tsing Hua University's Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Peter C. Bruce, is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. Kuber R. Deokar, is the Data Science Team Lead at UpThink Experts, India. He is also a faculty member at Statistics.com. Nitin R. Patel, PhD, is cofounder and lead researcher at Cytel Inc. He was also a co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a visiting professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

  Klappentext

  MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning-also known as data mining or predictive 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 with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting 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 fourth edition of Machine Learning for Business Analytics also includes:

• An expanded chapter on deep learning
• 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.

  Inhalt

  Foreword xix

  Preface to the Fourth Edition xxi

  Acknowledgments xxv

  PART I PRELIMINARIES

  CHAPTER 1 Introduction 3

  CHAPTER 2 Overview of the Machine Learning Process 15

  PART II DATA EXPLORATION AND DIMENSION REDUCTION

  CHAPTER 3 Data Visualization 59

  CHAPTER 4 Dimension Reduction 91

  PART III PERFORMANCE EVALUATION

  CHAPTER 5 Evaluating Predictive Performance 115

  PART IV PREDICTION AND CLASSIFICATION METHODS

  CHAPTER 6 Multiple Linear Regression 151

  CHAPTER 7 k-Nearest-Neighbors (k-NN) 169

  CHAPTER 8 The Naive Bayes Classifier 181

  CHAPTER 9 Classification and Regression Trees 197

  CHAPTER 10 Logistic Regression 229

  CHAPTER 11 Neural Nets 257

  CHAPTER 12 Discriminant Analysis 283

  CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303

  PART V INTERVENTION AND USER FEEDBACK

  CHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319

  PART VI MINING RELATIONSHIPS AMONG RECORDS

  CHAPTER 15 Association Rules and Collaborative Filtering 341

  CHAPTER 16 Cluster Analysis 369

  PART VII FORECASTING TIME SERIES

  CHAPTER 17 Handling Time Series 401

  CHAPTER 18 Regression-Based Forecasting 415

  CHAPTER 19 Smoothing Methods 445

  PART VIII DATA ANALYTICS

  CHAPTER 20 Social Network Analytics 467

  CHAPTER 21 Text Mining 487

  CHAPTER 22 Responsible Data Science 507

  PART IX CASES

  CHAPTER 23 Cases 537

  References 575

  Data Files Used in the Book 577

  Index 579