Machine Learning for Business Analytics

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.

• Foreword xixPreface xxAcknowledgments xxiiiPart I Preliminaries1 Introduction 31.1 What Is Business Analytics? 31.2 What Is Machine Learning? 51.3 Machine Learning, AI, and Related Terms 51.4 Big Data 61.5 Data Science 71.6 Why Are There So Many Different Methods? 81.7 Terminology and Notation 81.8 Road Maps to This Book 102 Overview of the Machine Learning Process 172.1 Introduction 172.2 Core Ideas in Machine Learning 182.3 The Steps in A Machine Learning Project 212.4 Preliminary Steps 222.5 Predictive Power and Overfitting 292.6 Building a Predictive Model with JMP Pro 342.7 Using JMP Pro for Machine Learning 422.8 Automating Machine Learning Solutions 432.9 Ethical Practice in Machine Learning 47Part II Data Exploration and Dimension Reduction3 Data Visualization 593.1 Introduction 593.2 Data Examples 613.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 623.4 Multidimensional Visualization 703.5 Specialized Visualizations 823.6 Summary: Major Visualizations and Operations, According to Machine Learning Goal 874 Dimension Reduction 914.1 Introduction 914.2 Curse of Dimensionality 924.3 Practical Considerations 92Part III Performance Evaluation5 Evaluating Predictive Performance 1175.1 Introduction 1185.2 Evaluating Predictive Performance 118Part IV Prediction and Classification Methods6 Multiple Linear Regression 1476.1 Introduction 1476.2 Explanatory vs. Predictive Modeling 1486.3 Estimating the Regression Equation and Prediction 1496.4 Variable Selection in Linear Regression 1557 k-Nearest Neighbors (k-NN) 1757.1 The k-NN Classifier (Categorical Outcome) 1758 The Naive Bayes Classifier 1898.1 Introduction 1899 Classification and Regression Trees 2059.1 Introduction 2069.2 Classification Trees 2079.3 Growing a Tree for Riding Mowers Example 2109.4 Evaluating the Performance of a Classification Tree 2159.5 Avoiding Overfitting 2199.6 Classification Rules from Trees 2229.7 Classification Trees for More Than Two Classes 2249.8 Regression Trees 2249.9 Advantages and Weaknesses of a Single Tree 2279.10 Improving Prediction: Random Forests and Boosted Trees 22910 Logistic Regression 23710.1 Introduction 23710.2 The Logistic Regression Model 23910.3 Example: Acceptance of Personal Loan 24010.4 Evaluating Classification Performance 24710.5 Variable Selection 24910.6 Logistic Regression for Multi-class Classification 25010.7 Example of Complete Analysis: Predicting Delayed Flights 25311 Neural Nets 26711.1 Introduction 26711.2 Concept and Structure of a Neural Network 26811.3 Fitting a Network to Data 26911.4 User Input in JMP Pro 28211.5 Exploring the Relationship Between Predictors and Outcome 28411.6 Deep Learning 28511.7 Advantages and Weaknesses of Neural Networks 28912 Discriminant Analysis 29312.1 Introduction 29312.2 Distance of an Observation from a Class 29512.3 From Distances to Propensities and Classifications 29712.4 Classification Performance of Discriminant Analysis 30012.5 Prior Probabilities 30112.6 Classifying More Than Two Classes 30312.7 Advantages and Weaknesses 30613 Generating, Comparing, and Combining Multiple Models 31113.1 Ensembles 31113.2 Automated Machine Learning (AutoML) 31713.3 Summary 322Part V Intervention and User Feedback14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 32714.1 Introduction 32714.2 A/B Testing 32814.3 Uplift (Persuasion) Modeling 33314.4 Reinforcement Learning 34014.5 Summary 344Part VI Mining Relationships Among Records15 Association Rules and Collaborative Filtering 34915.1 Association Rules 34915.2 Collaborative Filtering 36215.3 Summary 37016 Cluster Analysis 37516.1 Introduction 37516.2 Measuring Distance Between Two Records 37816.3 Measuring Distance Between Two Clusters 38316.4 Hierarchical (Agglomerative) Clustering 38516.5 Nonhierarchical Clustering: The K-Means Algorithm 394Part VII Forecasting Time Series17 Handling Time Series 40917.1 Introduction 40917.2 Descriptive vs. Predictive Modeling 41017.3 Popular Forecasting Methods in Business 41117.4 Time Series Components 41117.5 Data Partitioning and Performance Evaluation 41518 Regression-Based Forecasting 42318.1 A Model with Trend 42418.2 A Model with Seasonality 43018.3 A Model with Trend and Seasonality 43318.4 Autocorrelation and ARIMA Models 43319 Smoothing and Deep Learning Methods for Forecasting 45519.1 Introduction 45519.2 Moving Average 45619.3 Simple Exponential Smoothing 46119.4 Advanced Exponential Smoothing 46519.5 Deep Learning for Forecasting 470Part VIII Data Analytics20 Text Mining 48320.1 Introduction 48320.2 The Tabular Representation of Text: Document–Term Matrix and "Bag-of-Words" 48420.3 Bag-of-Words vs. Meaning Extraction at Document Level 48620.4 Preprocessing the Text 48620.5 Implementing Machine Learning Methods 49220.6 Example: Online Discussions on Autos and Electronics 49220.7 Example: Sentiment Analysis of Movie Reviews 50020.8 Summary 50221 Responsible Data Science 50521.1 Introduction 50521.2 Unintentional Harm 50621.3 Legal Considerations 50821.4 Principles of Responsible Data Science 50821.5 A Responsible Data Science Framework 51121.6 Documentation Tools 51421.7 Example: Applying the RDS Framework to the COMPAS Example 51721.8 Summary 526Part IX Cases22 Cases 53322.1 Charles Book Club 53322.2 German Credit 54122.3 Tayko Software Cataloger 54522.4 Political Persuasion 54822.5 Taxi Cancellations 55222.6 Segmenting Consumers of Bath Soap 55422.7 Catalog Cross-Selling 55722.8 Direct-Mail Fundraising 55922.9 Time Series Case: Forecasting Public Transportation Demand 56222.10 Loan Approval 564Index 573