Galit Shmueli (Taiwan National Tsing Hua University), Peter C. Bruce (USA Massachusetts Institute of Technology), Peter Gedeck (USA UVA School of Data Science)

Machine Learning for Business Analytics

Concepts, Techniques, and Applications in R

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119835170
• 2. Aufl.
• Seitenzahl: 688
• Erscheinungstermin: 8. Februar 2023
• Englisch
• Abmessung: 261mm x 185mm x 43mm
• Gewicht: 1632g
• ISBN-13: 9781119835172
• ISBN-10: 1119835178
• Artikelnr.: 66347538

Autorenporträt

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.

Inhaltsangabe

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