Machine Learning For Business Analytics (concepts, Techniques, And Applications With Analytic Solver Data Mining)

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.

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