Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver Data Mining (Hardcover, 4)

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