Machine Learning for Business Analytics – Concepts, Techniques, and Applications with Analytic Solver Data Mining, Fourth Edition

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.

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