PART I PRELIMINARIES CHAPTER 1 Introduction CHAPTER 2 Overview of the Machine Learning Process PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization CHAPTER 4 Dimension Reduction PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression CHAPTER 7 k-Nearest Neighbors (k-NN) CHAPTER 8 The Naive Bayes Classifier CHAPTER 9 Classification and Regression Trees CHAPTER 10 Logistic Regression CHAPTER 11 Neural Networks CHAPTER 12 Discriminant Analysis CHAPTER 13 Generating, Comparing, and Combining Multiple Models PART V INTERVENTION AND USER FEEDBACK CHAPTER 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning PART VI MINING RELATIONSHIPS AMONG RECORDS CHAPTER 15 Association Rules and Collaborative Filtering CHAPTER 16 Cluster Analysis PART VII FORECASTING TIME SERIES CHAPTER 17 Handling Time Series CHAPTER 18 Regression-Based Forecasting CHAPTER 19 Smoothing and Deep Learning Methods for Forecasting PART VIII DATA ANALYTICS CHAPTER 20 Social Network Analytics CHAPTER 21 Text Mining CHAPTER 22 Responsible Data Science PART IX CASES CHAPTER 23 Cases