Machine Learning for Business Analytics - Concepts, Techniques and Applications With Jmp Pro

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.

List of contents

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

About the author

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.

Summary

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.

Product details

• Authors: Muralidhara A, Muralidhara Anandamurthy, Peter C Bruce, Peter C. Bruce, Peter C. (Massachusetts Institute of Techno Bruce, Bruce Peter C., Nitin R. Patel, Patel Nitin R., Shmueli, Galit Shmueli, Galit (University of Maryland Shmueli, Mia L et a Stephens, Mia L. Stephens, Stephens Mia L.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 31.07.2023
• EAN: 9781119903833
• ISBN: 978-1-119-90383-3
• No. of pages: 608
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Datenanalyse, Data Mining, Statistics, Business Intelligence, business analytics, Business & management, data analysis, Wirtschaft u. Management, Data Mining Statistics, Theorie der Entscheidungsfindung, Decision Sciences, JMP (Software)