Galit Shmueli (College Park University of Maryland), Peter C. Bruce (Massachusetts Institute of Technology), Mia L. Stephens (JMP)

Machine Learning for Business Analytics

Concepts, Techniques and Applications with Jmp Pro

• Gebundenes Buch

Produktbeschreibung

MACHINE LEARNING FOR BUSINESS ANALYTICS

An up-to-date introduction to a market-leading platform for data analysis and machine learning

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. readers will also find:
_ Updated material which improves the book's usefulness as a reference for professionals beyond the classroom
_ Four new chapters, covering topics including Text Mining and Responsible Data Science
_ An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
_ A guide to JMP Pro(r)'s new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(r), 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119903830
• 2. Aufl.
• Seitenzahl: 608
• Erscheinungstermin: 9. Mai 2023
• Englisch
• Abmessung: 257mm x 185mm x 36mm
• Gewicht: 1385g
• ISBN-13: 9781119903833
• ISBN-10: 1119903831
• Artikelnr.: 66159022

Autorenporträt

Galit Shmueli, PhD is Distinguished Professor 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. Mia L. Stephens, M.S. is an Advisory Product Manager with JMP, driving the product vision and roadmaps for JMP® and JMP Pro®. Muralidhara Anandamurthy, PhD is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro®. Nitin R. Patel, PhD is cofounder and lead researcher at Cytel Inc. He is also a Fellow of the American Statistical Association and has served as a visiting professor at the Massachusetts Institute of Technology and Harvard University, among others.

Inhaltsangabe

Foreword xix

Preface xx

Acknowledgments xxiii

Part I Preliminaries

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

Statistical Modeling vs. Machine Learning 6

1.4 Big Data 6

1.5 Data Science 7

1.6 Why Are There So Many Different Methods? 8

1.7 Terminology and Notation 8

1.8 Road Maps to This Book 10

Order of Topics 12

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 19

2.3 The Steps in A Machine Learning Project 21

2.4 Preliminary Steps 22

Organization of Data 22

Sampling from a Database 22

Oversampling Rare Events in Classification Tasks 23

Preprocessing and Cleaning the Data 23

2.5 Predictive Power and Overfitting 29

Overfitting 29

Creation and Use of Data Partitions 31

2.6 Building a Predictive Model with JMP Pro 34

Predicting Home Values in a Boston Neighborhood 34

Modeling Process 36

2.7 Using JMP Pro for Machine Learning 42

2.8 Automating Machine Learning Solutions 43

Predicting Power Generator Failure 44

Uber's Michelangelo 45

2.9 Ethical Practice in Machine Learning 47

Machine Learning Software: The State of the Market by Herb

Edelstein 47

Problems 52

Part II Data Exploration and Dimension Reduction

3 Data Visualization 59

3.1 Introduction 59

3.2 Data Examples 61

Example 1: Boston Housing Data 61

Example 2: Ridership on Amtrak Trains 62

3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62

Distribution Plots: Boxplots and Histograms 64

Heatmaps 67

3.4 Multidimensional Visualization 70

Adding Variables: Color, Hue, Size, Shape, Multiple Panels,

Animation 70

Manipulations: Rescaling, Aggregation and Hierarchies, Zooming,

Filtering 73

Reference: Trend Line and Labels 77

Scaling Up: Large Datasets 79

Multivariate Plot: Parallel Coordinates Plot 80

Interactive Visualization 80

3.5 Specialized Visualizations 82

Visualizing Networked Data 82

Visualizing Hierarchical Data: More on Treemaps 83

Visualizing Geographical Data: Maps 84

3.6 Summary: Major Visualizations and Operations, According to

Machine Learning Goal 87

Prediction 87

Classification 87

Time Series Forecasting 87

Unsupervised Learning 88

Problems 89

4 Dimension Reduction 91

4.1 Introduction 91

4.2 Curse of Dimensionality 92

4.3 Practical Considerations 92

Problems 112

Part III Performance Evaluation

5 Evaluating Predictive Performance 117

5.1 Introduction 118

5.2 Evaluating Predictive Performance 118

Problems 142

Part IV Prediction and Classification Methods

6 Multiple Linear Regression 147

6.1 Introduction 147

6.2 Explanatory vs. Predictive Modeling 148

6.3 Estimating the Regression