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.
Table of Contents
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
