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 xixPreface to the Fourth Edition xxiAcknowledgments xxvPART I PRELIMINARIESCHAPTER 1 Introduction 3CHAPTER 2 Overview of the Machine Learning Process 15PART II DATA EXPLORATION AND DIMENSION REDUCTIONCHAPTER 3 Data Visualization 59CHAPTER 4 Dimension Reduction 91PART III PERFORMANCE EVALUATIONCHAPTER 5 Evaluating Predictive Performance 115PART IV PREDICTION AND CLASSIFICATION METHODSCHAPTER 6 Multiple Linear Regression 151CHAPTER 7 k-Nearest-Neighbors (k-NN) 169CHAPTER 8 The Naive Bayes Classifier 181CHAPTER 9 Classification and Regression Trees 197CHAPTER 10 Logistic Regression 229CHAPTER 11 Neural Nets 257CHAPTER 12 Discriminant Analysis 283CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303PART V INTERVENTION AND USER FEEDBACKCHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319PART VI MINING RELATIONSHIPS AMONG RECORDSCHAPTER 15 Association Rules and Collaborative Filtering 341CHAPTER 16 Cluster Analysis 369PART VII FORECASTING TIME SERIESCHAPTER 17 Handling Time Series 401CHAPTER 18 Regression-Based Forecasting 415CHAPTER 19 Smoothing Methods 445PART VIII DATA ANALYTICSCHAPTER 20 Social Network Analytics 467CHAPTER 21 Text Mining 487CHAPTER 22 Responsible Data Science 507PART IX CASESCHAPTER 23 Cases 537References 575Data Files Used in the Book 577Index 579

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 xixPreface to the Fourth Edition xxiAcknowledgments xxvPART I PRELIMINARIESCHAPTER 1 Introduction 3CHAPTER 2 Overview of the Machine Learning Process 15PART II DATA EXPLORATION AND DIMENSION REDUCTIONCHAPTER 3 Data Visualization 59CHAPTER 4 Dimension Reduction 91PART III PERFORMANCE EVALUATIONCHAPTER 5 Evaluating Predictive Performance 115PART IV PREDICTION AND CLASSIFICATION METHODSCHAPTER 6 Multiple Linear Regression 151CHAPTER 7 k-Nearest-Neighbors (k-NN) 169CHAPTER 8 The Naive Bayes Classifier 181CHAPTER 9 Classification and Regression Trees 197CHAPTER 10 Logistic Regression 229CHAPTER 11 Neural Nets 257CHAPTER 12 Discriminant Analysis 283CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303PART V INTERVENTION AND USER FEEDBACKCHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319PART VI MINING RELATIONSHIPS AMONG RECORDSCHAPTER 15 Association Rules and Collaborative Filtering 341CHAPTER 16 Cluster Analysis 369PART VII FORECASTING TIME SERIESCHAPTER 17 Handling Time Series 401CHAPTER 18 Regression-Based Forecasting 415CHAPTER 19 Smoothing Methods 445PART VIII DATA ANALYTICSCHAPTER 20 Social Network Analytics 467CHAPTER 21 Text Mining 487CHAPTER 22 Responsible Data Science 507PART IX CASESCHAPTER 23 Cases 537References 575Data Files Used in the Book 577Index 579

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 xixPreface to the Fourth Edition xxiAcknowledgments xxvPART I PRELIMINARIESCHAPTER 1 Introduction 3CHAPTER 2 Overview of the Machine Learning Process 15PART II DATA EXPLORATION AND DIMENSION REDUCTIONCHAPTER 3 Data Visualization 59CHAPTER 4 Dimension Reduction 91PART III PERFORMANCE EVALUATIONCHAPTER 5 Evaluating Predictive Performance 115PART IV PREDICTION AND CLASSIFICATION METHODSCHAPTER 6 Multiple Linear Regression 151CHAPTER 7 k-Nearest-Neighbors (k-NN) 169CHAPTER 8 The Naive Bayes Classifier 181CHAPTER 9 Classification and Regression Trees 197CHAPTER 10 Logistic Regression 229CHAPTER 11 Neural Nets 257CHAPTER 12 Discriminant Analysis 283CHAPTER 13 Generating, Comparing, and Combining Multiple Models 303PART V INTERVENTION AND USER FEEDBACKCHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319PART VI MINING RELATIONSHIPS AMONG RECORDSCHAPTER 15 Association Rules and Collaborative Filtering 341CHAPTER 16 Cluster Analysis 369PART VII FORECASTING TIME SERIESCHAPTER 17 Handling Time Series 401CHAPTER 18 Regression-Based Forecasting 415CHAPTER 19 Smoothing Methods 445PART VIII DATA ANALYTICSCHAPTER 20 Social Network Analytics 467CHAPTER 21 Text Mining 487CHAPTER 22 Responsible Data Science 507PART IX CASESCHAPTER 23 Cases 537References 575Data Files Used in the Book 577Index 579