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, 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, 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's new features and enhanced functionality Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 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.

Table 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 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 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 2.3 The Steps in A Machine Learning Project 21 2.4 Preliminary Steps 22 2.5 Predictive Power and Overfitting 29 2.6 Building a Predictive Model with JMP Pro 34 2.7 Using JMP Pro for Machine Learning 42 2.8 Automating Machine Learning Solutions 43 2.9 Ethical Practice in Machine Learning 47 Part II Data Exploration and Dimension Reduction 3 Data Visualization 59 3.1 Introduction 59 3.2 Data Examples 61 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62 3.4 Multidimensional Visualization 70 3.5 Specialized Visualizations 82 3.6 Summary: Major Visualizations and Operations, According to Machine Learning Goal 87 4 Dimension Reduction 91 4.1 Introduction 91 4.2 Curse of Dimensionality 92 4.3 Practical Considerations 92 Part III Performance Evaluation 5 Evaluating Predictive Performance 117 5.1 Introduction 118 5.2 Evaluating Predictive Performance 118 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 Equation and Prediction 149 6.4 Variable Selection in Linear Regression 155 7 k-Nearest Neighbors (k-NN) 175 7.1 The k-NN Classifier (Categorical Outcome) 175 8 The Naive Bayes Classifier 189 8.1 Introduction 189 9 Classification and Regression Trees 205 9.1 Introduction 206 9.2 Classification Trees 207 9.3 Growing a Tree for Riding Mowers Example 210 9.4 Evaluating the Performance of a Classification Tree 215 9.5 Avoiding Overfitting 219 9.6 Classification Rules from Trees 222 9.7 Classification Trees for More Than Two Classes 224 9.8 Regression Trees 224 9.9 Advantages and Weaknesses of a Single Tree 227 9.10 Improving Prediction: Random Forests and Boosted Trees 229 10 Logistic Regression 237 10.1 Introduction 237 10.2 The Logistic Regression Model 239 10.3 Example: Acceptance of Personal Loan 240 10.4 Evaluating Classification Performance 247 10.5 Variable Selection 249 10.6 Logistic Regression for Multi-class Classification 250 10.7 Example of Complete Analysis: Predicting Delayed Flights 253 11 Neural Nets 267 11.1 Introduction 267 11.2 Concept and Structure of a Neural Network 268 11.3 Fitting a Network to Data 269 11.4 User Input in JMP Pro 282 11.5 Exploring the Relationship Between Predictors and Outcome 284 11.6 Deep Learning 285 11.7 Advantages and Weaknesses of Neural Networks 289 12 Discriminant Analysis 293 12.1 Introduction 293 12.2 Distance of an Observation from a Class 295 12.3 From Distances to Propensities and Classifications 297 12.4 Classification Performance of Discriminant Analysis 300 12.5 Prior Probabilities 301 12.6 Classifying More Than Two Classes 303 12.7 Advantages and Weaknesses 306 13 Generating, Comparing, and Combining Multiple Models 311 13.1 Ensembles 311 13.2 Automated Machine Learning (AutoML) 317 13.3 Summary 322 Part V Intervention and User Feedback 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 327 14.1 Introduction 327 14.2 A/B Testing 328 14.3 Uplift (Persuasion) Modeling 333 14.4 Reinforcement Learning 340 14.5 Summary 344 Part VI Mining Relationships Among Records 15 Association Rules and Collaborative Filtering 349 15.1 Association Rules 349 15.2 Collaborative Filtering 362 15.3 Summary 370 16 Cluster Analysis 375 16.1 Introduction 375 16.2 Measuring Distance Between Two Records 378 16.3 Measuring Distance Between Two Clusters 383 16.4 Hierarchical (Agglomerative) Clustering 385 16.5 Nonhierarchical Clustering: The K-Means Algorithm 394 Part VII Forecasting Time Series 17 Handling Time Series 409 17.1 Introduction 409 17.2 Descriptive vs. Predictive Modeling 410 17.3 Popular Forecasting Methods in Business 411 17.4 Time Series Components 411 17.5 Data Partitioning and Performance Evaluation 415 18 Regression-Based Forecasting 423 18.1 A Model with Trend 424 18.2 A Model with Seasonality 430 18.3 A Model with Trend and Seasonality 433 18.4 Autocorrelation and ARIMA Models 433 19 Smoothing and Deep Learning Methods for Forecasting 455 19.1 Introduction 455 19.2 Moving Average 456 19.3 Simple Exponential Smoothing 461 19.4 Advanced Exponential Smoothing 465 19.5 Deep Learning for Forecasting 470 Part VIII Data Analytics 20 Text Mining 483 20.1 Introduction 483 20.2 The Tabular Representation of Text: Document–Term Matrix and "Bag-of-Words" 484 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 486 20.4 Preprocessing the Text 486 20.5 Implementing Machine Learning Methods 492 20.6 Example: Online Discussions on Autos and Electronics 492 20.7 Example: Sentiment Analysis of Movie Reviews 500 20.8 Summary 502 21 Responsible Data Science 505 21.1 Introduction 505 21.2 Unintentional Harm 506 21.3 Legal Considerations 508 21.4 Principles of Responsible Data Science 508 21.5 A Responsible Data Science Framework 511 21.6 Documentation Tools 514 21.7 Example: Applying the RDS Framework to the COMPAS Example 517 21.8 Summary 526 Part IX Cases 22 Cases 533 22.1 Charles Book Club 533 22.2 German Credit 541 22.3 Tayko Software Cataloger 545 22.4 Political Persuasion 548 22.5 Taxi Cancellations 552 22.6 Segmenting Consumers of Bath Soap 554 22.7 Catalog Cross-Selling 557 22.8 Direct-Mail Fundraising 559 22.9 Time Series Case: Forecasting Public Transportation Demand 562 22.10 Loan Approval 564 Index 573