GALIT SHMUELI, PHD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books. PETER C. BRUCE is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly). PETER GEDECK, PHD, is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com. NITIN R. PATEL, PhD, is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

Foreword by Gareth James xix Foreword by Ravi Bapna xxi Preface to the Python Edition xxiii Acknowledgments xxvii Part I Preliminaries Chapter 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining 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 9 1.8 Road Maps to This Book 11 Chapter 2 Overview of the Data Mining Process 15 2.1 Introduction 15 2.2 Core Ideas in Data Mining 16 2.3 The Steps in Data Mining 19 2.4 Preliminary Steps 21 2.5 Predictive Power and Overfitting 34 2.6 Building a Predictive Model 40 2.7 Using Python for Data Mining on a Local Machine 44 2.8 Automating Data Mining Solutions 45 2.9 Ethical Practice in Data Mining 47 Problems 56 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 61 3.1 Introduction 61 3.2 Data Examples 64 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 65 3.4 Multidimensional Visualization 74 3.5 Specialized Visualizations 88 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 93 Problems 97 Chapter 4 Dimension Reduction 99 4.1 Introduction 100 4.2 Curse of Dimensionality 100 4.3 Practical Considerations 100 4.4 Data Summaries 102 4.5 Correlation Analysis 105 4.6 Reducing the Number of Categories in Categorical Variables 106 4.7 Converting a Categorical Variable to a Numerical Variable 108 4.8 Principal Components Analysis 108 4.9 Dimension Reduction Using Regression Models 119 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 126 5.2 Evaluating Predictive Performance 126 5.3 Judging Classifier Performance 131 5.4 Judging Ranking Performance 144 5.5 Oversampling 149 Problems 155 Part IV Prediction and Classification Methods Chapter 6 Multiple Linear Regression 161 6.1 Introduction 162 6.2 Explanatory vs. Predictive Modeling 162 6.3 Estimating the Regression Equation and Prediction 164 6.4 Variable Selection in Linear Regression 169 Appendix: Using Statmodels 179 Problems 180 Chapter 7 k-Nearest Neighbors (kNN) 185 7.1 The k-NN Classifier (Categorical Outcome) 185 7.2 k-NN for a Numerical Outcome 193 7.3 Advantages and Shortcomings of k-NN Algorithms 195 Problems 197 Chapter 8 The Naive Bayes Classifier 199 8.1 Introduction 199 Example 1: Predicting Fraudulent Financial Reporting 201 8.2 Applying the Full (Exact) Bayesian Classifier 201 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 210 Problems 214 Chapter 9 Classification and Regression Trees 217 9.1 Introduction 218 9.2 Classification Trees 220 9.3 Evaluating the Performance of a Classification Tree 228 9.4 Avoiding Overfitting 232 9.5 Classification Rules from Trees 238 9.6 Classification Trees for More Than Two Classes 239 9.7 Regression Trees 239 9.8 Improving Prediction: Random Forests and Boosted Trees 243 9.9 Advantages and Weaknesses of a Tree 246 Problems 248 Chapter 10 Logistic Regression 251 10.1 Introduction 252 10.2 The Logistic Regression Model 253 10.3 Example: Acceptance of Personal Loan 255 10.4 Evaluating Classification Performance 261 10.5 Logistic Regression for Multi-class Classification 264 10.6 Example of Complete Analysis: Predicting Delayed Flights 269 Appendix: Using Statmodels 278 Problems 280 Chapter 11 Neural Nets 283 11.1 Introduction 284 11.2 Concept and Structure of a Neural Network 284 11.3 Fitting a Network to Data 285 11.4 Required User Input 297 11.5 Exploring the Relationship Between Predictors and Outcome 299 11.6 Deep Learning 299 11.7 Advantages and Weaknesses of Neural Networks 305 Problems 306 Chapter 12 Discriminant Analysis 309 12.1 Introduction 310 12.2 Distance of a Record from a Class 311 12.3 Fisher's Linear Classification Functions 314 12.4 Classification Performance of Discriminant Analysis 317 12.5 Prior Probabilities 318 12.6 Unequal Misclassification Costs 319 12.7 Classifying More Than Two Classes 319 12.8 Advantages and Weaknesses 322 Problems 324 Chapter 13 Combining Methods: Ensembles and Uplift Modeling 327 13.1 Ensembles 328 13.2 Uplift (Persuasion) Modeling 334 13.3 Summary 340 Problems 341 Part V Mining Relationships among Records Chapter 14 Association Rules and Collaborative Filtering 345 14.1 Association Rules 346 14.2 Collaborative Filtering 357 14.3 Summary 368 Problems 370 Chapter 15 Cluster Analysis 375 15.1 Introduction 376 15.2 Measuring Distance Between Two Records 379 15.3 Measuring Distance Between Two Clusters 385 15.4 Hierarchical (Agglomerative) Clustering 387 15.5 Non-Hierarchical Clustering: The k-Means Algorithm 395 Problems 401 Part VI Forecasting Time Series Chapter 16 Handling Time Series 407 16.1 Introduction 408 16.2 Descriptive vs. Predictive Modeling 409 16.3 Popular Forecasting Methods in Business 409 16.4 Time Series Components 410 16.5 Data-Partitioning and Performance Evaluation 415 Problems 419 Chapter 17 Regression-Based Forecasting 423 17.1 A Model with Trend 424 17.2 A Model with Seasonality 429 17.3 A Model with Trend and Seasonality 432 17.4 Autocorrelation and ARIMA Models 433 Problems 442 Chapter 18 Smoothing Methods 451 18.1 Introduction 452 18.2 Moving Average 452 18.3 Simple Exponential Smoothing 457 18.4 Advanced Exponential Smoothing 460 Problems 464 Part VII Data Analytics Chapter 19 Social Network Analytics 473 19.1 Introduction 473 19.2 Directed vs. Undirected Networks 475 19.3 Visualizing and Analyzing Networks 476 19.4 Social Data Metrics and Taxonomy 480 19.5 Using Network Metrics in Prediction and Classification 485 19.6 Collecting Social Network Data with Python 491 19.7 Advantages and Disadvantages 491 Problems 494 Chapter 20 Text Mining 495 20.1 Introduction 496 20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words'' 496 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 497 20.4 Preprocessing the Text 498 20.5 Implementing Data Mining Methods 506 20.6 Example: Online Discussions on Autos and Electronics 506 20.7 Summary 510 Problems 511 Part VIII Cases Chapter 21 Cases 515 21.1 Charles Book Club 515 21.2 German Credit 522 21.3 Tayko Software Cataloger 527 21.4 Political Persuasion 531 21.5 Taxi Cancellations 535 21.6 Segmenting Consumers of Bath Soap 537 21.7 Direct-Mail Fundraising 541 21.8 Catalog Cross-Selling 544 21.9 Time Series Case: Forecasting Public Transportation Demand 546 References 549 Data Files Used in the Book 551 Python Utilities Functions 555 Index 565