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, 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.

Table of Contents:
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 Problems 57 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 Problems 120 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 Problems 158 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 Problems 184 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 Appendix: Computing Distances Between Records in RapidMiner 203 Problems 205 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 224 Problems 226 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 9.9 Advantages and Weaknesses of a Tree 261 Problems 265 Chapter 10 Logistic Regression 269 10.1 Introduction 269 10.2 The Logistic Regression Model 271 10.3 Example: Acceptance of Personal Loan 272 10.4 Logistic Regression for Multi-class Classification 283 10.5 Example of Complete Analysis: Predicting Delayed Flights 286 Appendix: Logistic Regression for Ordinal Classes 299 Problems 301 Chapter 11 Neural Networks 305 11.1 Introduction 306 11.2 Concept and Structure of a Neural Network 306 11.3 Fitting a Network to Data 307 11.4 Required User Input 321 11.5 Exploring the Relationship Between Predictors and Target Attribute 322 11.6 Deep Learning 323 11.7 Advantages and Weaknesses of Neural Networks 334 Problems 335 Chapter 12 Discriminant Analysis 337 12.1 Introduction 337 12.2 Distance of a Record from a Class 340 12.3 Fisher’s Linear Classification Functions 341 12.4 Classification Performance of Discriminant Analysis 346 12.5 Prior Probabilities 348 12.6 Unequal Misclassification Costs 348 12.7 Classifying More Than Two Classes 349 12.8 Advantages and Weaknesses 351 Problems 355 Chapter 13 Generating, Comparing, and Combining Multiple Models 359 13.1 Automated Machine Learning (AutoML) 359 13.2 Explaining Model Predictions 367 13.3 Ensembles 373 13.4 Summary 381 Problems 383 Part V Intervention and User Feedback Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 387 14.1 A/B Testing 387 14.2 Uplift (Persuasion) Modeling 393 14.3 Reinforcement Learning 400 14.4 Summary 405 Problems 406 Part VI Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 409 15.1 Association Rules 409 15.2 Collaborative Filtering 424 15.3 Summary 438 Problems 440 Chapter 16 Cluster Analysis 445 16.1 Introduction 445 16.2 Measuring Distance Between Two Records 449 16.3 Measuring Distance Between Two Clusters 455 16.4 Hierarchical (Agglomerative) Clustering 457 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 466 Problems 473 Part VII Forecasting Time Series Chapter 17 Handling Time Series 479 17.1 Introduction 480 17.2 Descriptive vs. Predictive Modeling 481 17.3 Popular Forecasting Methods in Business 481 17.4 Time Series Components 482 17.5 Data Partitioning and Performance Evaluation 486 Problems 493 Chapter 18 Regression-Based Forecasting 497 18.1 A Model with Trend 498 18.2 A Model with Seasonality 505 18.3 A Model with Trend and Seasonality 508 18.4 Autocorrelation and ARIMA Models 509 Problems 521 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 533 19.1 Smoothing Methods: Introduction 534 19.2 Moving Average 534 19.3 Simple Exponential Smoothing 540 19.4 Advanced Exponential Smoothing 545 19.5 Deep Learning for Forecasting 549 Problems 553 Part VIII Data Analytics Chapter 20 Social Network Analytics 563 20.1 Introduction 563 20.2 Directed vs. Undirected Networks 564 20.3 Visualizing and Analyzing Networks 567 20.4 Social Data Metrics and Taxonomy 571 20.5 Using Network Metrics in Prediction and Classification 576 20.6 Collecting Social Network Data with RapidMiner 584 20.7 Advantages and Disadvantages 584 Problems 587 Chapter 21 Text Mining 589 21.1 Introduction 589 21.2 The Tabular Representation of Text: Term–Document Matrix and “Bag-of-Words’’ 590 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 592 21.4 Preprocessing the Text 593 21.5 Implementing Machine Learning Methods 602 21.6 Example: Online Discussions on Autos and Electronics 602 21.7 Example: Sentiment Analysis of Movie Reviews 607 21.8 Summary 614 Problems 615 Chapter 22 Responsible Data Science 617 22.1 Introduction 617 22.2 Unintentional Harm 618 22.3 Legal Considerations 620 22.4 Principles of Responsible Data Science 621 22.5 A Responsible Data Science Framework 624 22.6 Documentation Tools 628 22.7 Example: Applying the RDS Framework to the COMPAS Example 631 22.8 Summary 641 Problems 643 Part IX Cases Chapter 23 Cases 647 23.1 Charles Book Club 647 23.2 German Credit 654 23.3 Tayko Software Cataloger 659 23.4 Political Persuasion 663 23.5 Taxi Cancellations 667 23.6 Segmenting Consumers of Bath Soap 669 23.7 Direct-Mail Fundraising 673 23.8 Catalog Cross-Selling 676 23.9 Time Series Case: Forecasting Public Transportation Demand 678 23.10 Loan Approval 680 References 683 Data Files Used in the Book 687 Index 689