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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,…mehr
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.
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Autorenporträt
Galit Shmueli, PhD, 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, PhD, 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, PhD, 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.
Inhaltsangabe
Foreword by Ravi Bapna xxi
Preface to the RapidMiner Edition xxiii
Acknowledgments xxvii
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? 9
1.7 Terminology and Notation 9
1.8 Road Maps to This Book 12
1.9 Using RapidMiner Studio 14
CHAPTER 2 Overview of the Machine Learning Process 19
2.1 Introduction 19
2.2 Core Ideas in Machine Learning 20
2.3 The Steps in a Machine Learning Project 23
2.4 Preliminary Steps 25
2.5 Predictive Power and Overfitting 32
2.6 Building a Predictive Model with RapidMiner 37
2.7 Using RapidMiner for Machine Learning 45
2.8 Automating Machine Learning Solutions 47
2.9 Ethical Practice in Machine Learning 52
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization 63
3.1 Introduction 63
3.2 Data Examples 65
3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66
3.4 Multidimensional Visualization 75
3.5 Specialized Visualizations 87
3.6 Summary: Major Visualizations and Operations, by Machine Learning Goal 92
CHAPTER 4 Dimension Reduction 97
4.1 Introduction 97
4.2 Curse of Dimensionality 98
4.3 Practical Considerations 98
4.4 Data Summaries 100
4.5 Correlation Analysis 103
4.6 Reducing the Number of Categories in Categorical Attributes 105
4.7 Converting a Categorical Attribute to a Numerical Attribute 107
4.8 Principal Component Analysis 107
4.9 Dimension Reduction Using Regression Models 117
4.10 Dimension Reduction Using Classification and Regression Trees 119
PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance 125
5.1 Introduction 125
5.2 Evaluating Predictive Performance 126
5.3 Judging Classifier Performance 131
5.4 Judging Ranking Performance 146
5.5 Oversampling 151
PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression 163
6.1 Introduction 163
6.2 Explanatory vs. Predictive Modeling 164
6.3 Estimating the Regression Equation and Prediction 166
6.4 Variable Selection in Linear Regression 171
CHAPTER 7 k-Nearest Neighbors (k-NN) 189
7.1 The k-NN Classifier (Categorical Label) 189
7.2 k-NN for a Numerical Label 200
7.3 Advantages and Shortcomings of k-NN Algorithms 202
CHAPTER 8 The Naive Bayes Classifier 209
8.1 Introduction 209
8.2 Applying the Full (Exact) Bayesian Classifier 211
8.3 Solution: Naive Bayes 213
8.4 Advantages and Shortcomings of the Naive Bayes Classifier 223
CHAPTER 9 Classification and Regression Trees 229
9.1 Introduction 229
9.2 Classification Trees 232
9.3 Evaluating the Performance of a Classification Tree 240
9.4 Avoiding Overfitting 245
9.5 Classification Rules from Trees 255
9.6 Classification Trees for More Than Two Classes 256
9.7 Regression Trees 256
9.8 Improving Prediction: Random Forests and Boosted Trees 259
