Preface to the Second Python 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? 8

1.7 Terminology and Notation 9

1.8 Road Maps to This Book 12

Order of Topics 13

Chapter 2 Overview of the Machine Learning Process 17

2.1 Introduction 18

2.2 Core Ideas in Machine Learning 18

2.3 The Steps in a Machine Learning Project 22

2.4 Preliminary Steps 23

2.5 Predictive Power and Overfitting 37

2.6 Building a Predictive Model 43

2.7 Using Python for Machine Learning on a Local Machine 49

2.8 Automating Machine Learning Solutions 49

2.9 Ethical Practice in Machine Learning 54

Problems 55

Part II Data Exploration and Dimension Reduction

Chapter 3 Data Visualization 61

3.1 Uses of Data Visualization 62

3.2 Data Examples 64

3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66

3.4 Multidimensional Visualization 75

3.5 Specialized Visualizations 90

Problems 98

Chapter 4 Dimension Reduction 101

4.1 Introduction 102

4.2 Curse of Dimensionality 102

4.3 Practical Considerations 103

4.4 Data Summaries 103

4.5 Correlation Analysis 108

4.6 Reducing the Number of Categories in Categorical Variables 109

4.7 Converting a Categorical Variable to a Numerical Variable 109

4.8 Principal Component Analysis 111

4.9 Dimension Reduction Using Regression Models 121

4.10 Dimension Reduction Using Classification and Regression Trees 121

Problems 123

Part III Performance Evaluation

Chapter 5 Evaluating Predictive Performance 129

5.1 Introduction 130

5.2 Evaluating Predictive Performance 131

5.3 Judging Classifier Performance 137

5.4 Judging Ranking Performance 150

5.5 Oversampling 156

Problems 162

Part IV Prediction and Classification Methods

Chapter 6 Multiple Linear Regression 167

6.1 Introduction 168

6.2 Explanatory vs. Predictive Modeling 168

6.3 Estimating the Regression Equation and Prediction 170

6.4 Variable Selection in Linear Regression 176

Problems 188

Chapter 7 k-Nearest Neighbors (k-NN) 193

7.1 The k-NN Classifier (Categorical Outcome) 194

7.2 k-NN for a Numerical Outcome 203

7.3 Advantages and Shortcomings of k-NN Algorithms 205

Problems 207

Chapter 8 The Naive Bayes Classifier 209

8.1 Introduction 209

8.2 Applying the Full (Exact) Bayesian Classifier 212

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 230

9.2 Classification Trees 232

9.3 Evaluating the Performance of a Classification Tree 241

9.4 Avoiding Overfitting 246

9.5 Classification Rules from Trees 252

9.6 Classification Trees for More Than Two Classes 252

9.7 Regression Trees 253

9.8 Advantages and Weaknesses of a Tree 256

9.9 Improving Prediction: Random Forests and Boosted Trees 258

Problems 264

Chapter 10 Logistic Regression 267

10.1 Introduction 268

10.2 The Logistic Regression Model 269

10.3 Example: Acceptance of Personal Loan 272

10.4 Evaluating Classification Performance 277

10.5 Variable Selection 280

10.6 Logistic Regression for Multi-Class Classification 281

10.7 Example of Complete Analysis: Predicting Delayed Flights 285

Problems 298

Chapter 11 Neural Nets 301

11.1 Introduction 302

11.2 Concept and Structure of a Neural Network 302

11.3 Fitting a Network to Data 303

11.4 Required User Input 316

11.5 Exploring the Relationship Between Predictors and Outcome 317

11.6 Deep Learning 318

11.7 Advantages and Weaknesses of Neural Networks 329

Problems 331

Chapter 12 Discriminant Analysis 333

12.1 Introduction 334

12.2 Distance of a Record from a Class 336

12.3 Fisher’s Linear Classification Functions 337

12.4 Classification Performance of Discriminant Analysis 341

12.5 Prior Probabilities 342

12.6 Unequal Misclassification Costs 342

12.7 Classifying More Than Two Classes 344

12.8 Advantages and Weaknesses 347

Problems 348

Chapter 13 Generating, Comparing, and Combining Multiple Models 351

13.1 Ensembles 352

13.2 Automated Machine Learning (AutoML) 359

13.3 Explaining Model Predictions 365

13.4 Summary 366

Problems 368

Chapter 14 Experiments, Uplift Models, and Reinforcement Learning 371

14.1 A/B Testing 372

14.2 Uplift (Persuasion) Modeling 377

14.3 Reinforcement Learning 384

14.4 Summary 393

Problems 395

Part V Mining Relationships Among Records

Chapter 15 Association Rules and Collaborative Filtering 399

15.1 Association Rules 400

15.2 Collaborative Filtering 413

15.3 Summary 427

Problems 429

Chapter 16 Cluster Analysis 433

16.1 Introduction 434

16.2 Measuring Distance Between Two Records 437

16.3 Measuring Distance Between Two Clusters 443

16.4 Hierarchical (Agglomerative) Clustering 445

16.5 Non-Hierarchical Clustering: The k-Means Algorithm 453

Problems 459

Part VI Forecasting Time Series

Chapter 17 Handling Time Series 463

17.1 Introduction 464

17.2 Descriptive vs. Predictive Modeling 465

17.3 Popular Forecasting Methods in Business 465

17.4 Time Series Components 466

17.5 Data Partitioning and Performance Evaluation 470

Problems 474

Chapter 18 Regression-Based Forecasting 477

18.1 A Model with Trend 478

18.2 A Model with Seasonality 484

18.3 A Model with Trend and Seasonality 486

18.4 Autocorrelation and ARIMA Models 488

Problems 498

Chapter 19 Smoothing and Deep Learning Methods for Forecasting 509

19.1 Smoothing Methods: Introduction 510

19.2 Moving Average 510

19.3 Simple Exponential Smoothing 515

19.4 Advanced Exponential Smoothing 518

19.5 Deep Learning for Forecasting 521

Problems 527

Part VII Data Analytics

Chapter 20 Social Network Analytics 537

20.1 Introduction 538

20.2 Directed vs. Undirected Networks 538

20.3 Visualizing and Analyzing Networks 539

20.4 Social Data Metrics and Taxonomy 544

20.5 Using Network Metrics in Prediction and Classification 550

20.6 Business Uses of Social Network Analysis 556

20.7 Summary 557

Problems 559

Chapter 21 Text Mining 561

21.1 Introduction 562

21.2 The Tabular Representation of Text 562

21.3 Bag-of-Words vs. Meaning Extraction at Document Level 563

21.4 Preprocessing the Text 564

21.5 Implementing Machine Learning Methods 573

21.6 Example: Online Discussions on Autos and Electronics 573

21.7 Deep Learning Approaches 577

21.8 Example: Sentiment Analysis of Movie Reviews 578

21.9 Summary 581

Problems 584

Chapter 22 Responsible Data Science 587

22.1 Introduction 588

22.2 Unintentional Harm 589

22.3 Legal Considerations 591

22.4 Principles of Responsible Data Science 592

22.5 A Responsible Data Science Framework 595

22.6 Documentation Tools 599

22.7 Example: Applying the RDS Framework to the COMPAS Example 603

22.8 Summary 613

Problems 614

Chapter 23 Generative AI 617

23.1 The Transformative Power of Generative AI 617

23.2 What is Generative AI? 619

23.3 Data and Infrastructure Requirements 621

23.4 Adapting Models for Specific Purposes 623

23.5 Prompt Engineering 624

23.6 Uses of Generative AI 625

23.7 Caveats and Concerns 629

23.8 Summary 631

Problems 633

Part VIII Cases

Chapter 24 Cases 639

24.1 Charles Book Club 639

24.2 German Credit 646

24.3 Tayko Software Cataloger 651

24.4 Political Persuasion 655

24.5 Taxi Cancellations 659

24.7 Direct-Mail Fundraising 665

24.8 Catalog Cross-Selling 668

24.9 Time-Series Case: Forecasting Public Transportation Demand 670

24.10 Loan Approval 672

References 675

Index 677