Machine Learning for Business Analytics - Concepts, Techniques, and Applications in R

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 R 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 second R edition of Machine Learning for Business Analytics. This edition also includes:
* A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R
* 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.

List of contents

Foreword by Ravi Bapna xix
 
Foreword by Gareth James xxi
 
Preface to the Second R Edition xxiii
 
Acknowledgments xxvi
 
Part I Preliminaries
 
Chapter 1 Introduction 3
 
1.1 What Is Business Analytics? 3
 
1.2 What Is Machine Learning? 5
 
1.3 Machine Learning, AI, and Related Terms 5
 
1.4 Big Data 7
 
1.5 Data Science 8
 
1.6 Why Are There So Many Different Methods? 8
 
1.7 Terminology and Notation 9
 
1.8 Road Maps to This Book 11
 
Order of Topics 13
 
Chapter 2 Overview of the Machine Learning Process 17
 
2.1 Introduction 17
 
2.2 Core Ideas in Machine Learning 18
 
Classification 18
 
Prediction 18
 
Association Rules and Recommendation Systems 18
 
Predictive Analytics 19
 
Data Reduction and Dimension Reduction 19
 
Data Exploration and Visualization 19
 
Supervised and Unsupervised Learning 20
 
2.3 The Steps in a Machine Learning Project 21
 
2.4 Preliminary Steps 23
 
Organization of Data 23
 
Predicting Home Values in the West Roxbury Neighborhood 23
 
Loading and Looking at the Data in R 24
 
Sampling from a Database 26
 
Oversampling Rare Events in Classification Tasks 27
 
Preprocessing and Cleaning the Data 28
 
2.5 Predictive Power and Overfitting 35
 
Overfitting 36
 
Creating and Using Data Partitions 38
 
2.6 Building a Predictive Model 41
 
Modeling Process 41
 
2.7 Using R for Machine Learning on a Local Machine 46
 
2.8 Automating Machine Learning Solutions 47
 
Predicting Power Generator Failure 48
 
Uber's Michelangelo 50
 
2.9 Ethical Practice in Machine Learning 52
 
Machine Learning Software: The State of the Market (by Herb Edelstein) 53
 
Problems 57
 
Part II Data Exploration and Dimension Reduction
 
Chapter 3 Data Visualization 63
 
3.1 Uses of Data Visualization 63
 
Base R or ggplot? 65
 
3.2 Data Examples 65
 
Example 1: Boston Housing Data 65
 
Example 2: Ridership on Amtrak Trains 67
 
3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 67
 
Distribution Plots: Boxplots and Histograms 70
 
Heatmaps: Visualizing Correlations and Missing Values 73
 
3.4 Multidimensional Visualization 75
 
Adding Variables: Color, Size, Shape, Multiple Panels, and Animation 76
 
Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 79
 
Reference: Trend Lines and Labels 83
 
Scaling Up to Large Datasets 85
 
Multivariate Plot: Parallel Coordinates Plot 85
 
Interactive Visualization 88
 
3.5 Specialized Visualizations 91
 
Visualizing Networked Data 91
 
Visualizing Hierarchical Data: Treemaps 93
 
Visualizing Geographical Data: Map Charts 95
 
3.6 Major Visualizations and Operations, by Machine Learning Goal 97
 
Prediction 97
 
Classification 97
 
Time Series Forecasting 97
 
Unsupervised Learning 98
 
Problems 99
 
Chapter 4 Dimension Reduction 101
 
4.1 Introduction 101
 
4.2 Curse of Dimensionality 102
 
4.3 Practical Considerations 102
 
Example 1: House Prices in Boston 103
 
4.4 Data Summaries 103
 
Summary Statistics 104
 
Aggregation and Pivot Tables 104
 
4.5 Correlation Analysis 107
 
4.6 Reducing the Number of Categories in Categorical Variables 109
 
4.7 Converting a Categorical

About the author

Galit Shmueli, PhD, is Distinguished Professor and Institute Director at National Tsing Hua University's Institute of Service Science. 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. Peter Gedeck, PhD, is Senior Data Scientist at Collaborative Drug Discovery and teaches at statistics.com and the UVA School of Data Science. His specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Inbal Yahav, PhD, is a Senior Lecturer in The Coller School of Management at Tel Aviv University, Israel. Her work focuses on the development and adaptation of statistical models for use by researchers in the field of information systems. Nitin R. Patel, PhD, is Co-founder 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, USA.

Product details

• Authors: Peter C Bruce, Peter C. Bruce, Bruce Peter C., Peter Gedeck, Peter et al Gedeck, Nitin R. Patel, Patel Nitin R., Galit Shmueli, Galit (National Tsing Hua University Shmueli, Inbal Yahav
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 08.02.2023
• EAN: 9781119835172
• ISBN: 978-1-119-83517-2
• No. of pages: 688
• Subjects: Natural sciences, medicine, IT, technology > Mathematics; Statistik, Data Mining, Maschinelles Lernen, Statistics, Neuronale Netze, business analytics, Business & management, Neural Networks, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Wirtschaft u. Management, Data Mining Statistics, Theorie der Entscheidungsfindung, Decision Sciences