Machine Learning for Business Analytics

Machine learning—also known as data mining or data 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 in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation 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 is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes:

• A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner
• Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years
• An expanded chapter focused on discussion of deep learning techniques
• 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

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

About the Authors

Galit Shmueli, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science, College of Technology Management. 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.

Amit V. Deokar, is Associate Dean of Undergraduate Programs and an Associate Professor of Management Information Systems at the Manning School of Business at University of Massachusetts Lowell. Since 2006, he has developed and taught courses in business analytics, with expertise in using the RapidMiner platform. He is an Association for Information Systems Distinguished Member Cum Laude.

Nitin R. Patel, 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.