Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro(R), a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) also includes: * Detailed summaries that supply an outline of key topics at the beginning of each chapter * End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material * Data-rich case studies to illustrate various applications of data mining techniques * A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. 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 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.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 and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner (R), Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP(R), a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder 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. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition, also published by Wiley.
Dedication i Foreword xvii Preface xviii Acknowledgments xx 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 Order of Topics 12 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 20 2.5 Predictive Power and Overfitting 28 2.6 Building a Predictive Model with JMP Pro 33 2.7 Using JMP Pro for Data Mining 42 2.8 Automating Data Mining Solutions 42 Data Mining Software Tools (Herb Edelstein) 44 Problems 47 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 52 3.1 Uses of Data Visualization 52 3.2 Data Examples 54 Example 1: Boston Housing Data 54 Example 2: Ridership on Amtrak Trains 55 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 55 Distribution Plots 58 Heatmaps: visualizing correlations and missing values 61 3.4 Multi-Dimensional Visualization 63 Adding Variables: Color, Hue, Size, Shape, Multiple Panels, Animation 63 Manipulations: Re-scaling, Aggregation and Hierarchies, Zooming and Panning, Filtering 67 Reference: Trend Line and Labels 70 Scaling Up: Large Datasets 72 Multivariate Plot: Parallel Coordinates Plot 73 Interactive Visualization 74 3.5 Specialized Visualizations 76 Visualizing Networked Data 76 Visualizing Hierarchical Data: Treemaps 77 Visualizing Geographical Data: Maps 78 3.6 Summary of Major Visualizations and Operations, According to Data Mining Goal 80 Prediction 80 Classification 81 Time Series Forecasting 81 Unsupervised Learning 82 Problems 83 CHAPTER 4 Dimension Reduction 85 4.1 Introduction 85 4.2 Curse of Dimensionality 86 4.3 Practical Considerations 86 Example 1: House Prices in Boston 87 4.4 Data Summaries 88 4.5 Correlation Analysis 91 4.6 Reducing the Number of Categories in Categorical Variables 92 4.7 Converting A Categorical Variable to A Continuous Variable 94 4.8 Principal Components Analysis 94 Example 2: Breakfast Cereals 95 Principal Components 101 Normalizing the Data 102 Using Principal Components for Classification and Prediction 104 4.9 Dimension Reduction Using Regression Models 104 4.10 Dimension Reduction Using Classification and Regression Trees 106 Problems 107 PART III PERFORMANCE EVALUATION CHAPTER 5 Evaluating Predictive Performance 111 5.1 Introduction 111 5.2 Evaluating Predictive Performance 112 Benchmark: The Average 112 Prediction Accuracy Measures 113 5.3 Judging Classifier Performance 115 Benchmark: The Naive Rule 115 Class Separation 115 The Classification Matrix 116 Using the Validation Data 117 Accuracy Measures 117 Cutoff for Classification 118 Performance in Unequal Importance of Classes 122 Asymmetric Misclassification Costs 123 5.4 Judging Ranking Performance 127 5.5 Oversampling 131 Problems 138 PART IV PREDICTION AND CLASSIFICATION METHODS CHAPTER 6 Multiple Linear Regression 141 6.1 Introduction 141 6.2 Explanatory vs. Predictive Modeling 142 6.3 Estimating the Regression Equation and Prediction 143 Example: Predicting the Price of Used Toyota Corolla Automobiles . 144 6.4 Variable Selection in Linear Regression 149 Reducing the Number of Predictors 149 How to Reduce the Number of Predictors 150 Manual Variable Selection 151 Automated Variable Selection 151 Problems 160 CHAPTER 7 k-Nearest Neighbors (kNN) 165 7.1 The k-NN Classifier (categorical outcome) 165 Determining Neighbors 165 Classification Rule 166 Example: Riding Mowers 166 Choosing k 167 Setting the Cutoff Value 169 7.2 k-NN for a Numerical Response 171 7.3 Advantages and Shortcomings of k-NN Algorithms 172 Problems 174 CHAPTER 8 The Naive Bayes Classifier 176 8.1 Introduction 176 Example 1: Predicting Fraudulent Financial Reporting 177 8.2 Applying the Full (Exact) Bayesian Classifier 178 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 187 Advantages and Shortcomings of the naive Bayes Classifier 187 Problems 191 CHAPTER 9 Classification and Regression Trees 194 9.1 Introduction 194 9.2 Classification Trees 195 Example 1: Riding Mowers 196 9.3 Growing a Tree 198 Growing a Tree Example 198 Growing a Tree with CART 203 9.4 Evaluating the Performance of a Classification Tree 203 Example 2: Acceptance of Personal Loan 203 9.5 Avoiding Overfitting 204 Stopping Tree Growth: CHAID 205 Pruning the Tree 207 9.6 Classification Rules from Trees 208 9.7 Classification Trees for More Than two Classes 210 9.8 Regression Trees 210 Prediction 213 Evaluating Performance 214 9.9 Advantages and Weaknesses of a Tree 214 9.10 Improving Prediction: Multiple Trees 216 9.11 CART, and Measures of Impurity 218 Measuring Impurity 218 Problems 221 CHAPTER 10 Logistic Regression 224 10.1 Introduction 224 10.2 The Logistic Regression Model 226 Example: Acceptance of Personal Loan 227 Model with a Single Predictor 229 Estimating the Logistic Model from Data: Computing Parameter Estimates 231 10.3 Evaluating Classification Performance 234 Variable Selection 236 10.4 Example of Complete Analysis: Predicting Delayed Flights 237 Data Preprocessing 240 Model Fitting, Estimation and Interpretation - A Simple Model 240 Model Fitting, Estimation and Interpretation - The Full Model 241 Model Performance 243 Variable Selection 245 10.5 Appendix: Logistic Regression for Profiling 249 Appendix A: Why Linear Regression Is Inappropriate for a Categorical Response 249 Appendix B: Evaluating Explanatory Power 250 Appendix C: Logistic Regression for More Than Two Classes 253 Problems 257 CHAPTER 11 Neural Nets 260 11.1 Introduction 260 11.2 Concept and Structure of a Neural Network 261 11.3 Fitting a Network to Data 261 Example 1: Tiny Dataset 262 Computing Output of Nodes 263 Preprocessing the Data 266 Training the Model 267 Using the Output for Prediction and Classification 272 Example 2: Classifying Accident Severity 273 Avoiding overfitting 275 11.4 User Input in JMP Pro 277 11.5 Exploring the Relationship Between Predictors and Response 280 11.6 Advantages and Weaknesses of Neural Networks 281 Problems 282 CHAPTER 12 Discriminant Analysis 284 12.1 Introduction 284 Example 1: Riding Mowers 285 Example 2: Personal Loan Acceptance 285 12.2 Distance of an Observation from a Class 286 12.3 From Distances to Propensities and Classifications 288 12.4 Classification Performance of Discriminant Analysis 292 12.5 Prior Probabilities 293 12.6 Classifying More Than Two Classes 294 Example 3: Medical Dispatch to Accident Scenes 294 12.7 Advantages and Weaknesses 296 Problems 299 CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 302 13.1 Ensembles 303 Why Ensembles Can Improve Predictive Power 303 Simple Averaging 305 Bagging 306 Boosting 306 Advantages and Weaknesses of Ensembles 307 13.2 Uplift (Persuasion) Modeling 308 A-B Testing 308 Uplift 308 Gathering the Data 309 A Simple Model 310 Modeling Individual Uplift 311 Using the Results of an Uplift Model 312 Creating Uplift Models in JMP Pro 313 13.3 Summary 315 Problems 316 PART V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Cluster Analysis 320 14.1 Introduction 320 Example: Public Utilities 322 14.2 Measuring Distance Between Two Observations 324 Euclidean Distance 324 Normalizing Numerical Measurements 324 Other Distance Measures for Numerical Data 326 Distance Measures for Categorical Data 327 Distance Measures for Mixed Data 327 14.3 Measuring Distance Between Two Clusters 328 14.4 Hierarchical (Agglomerative) Clustering 330 Single Linkage 332 Complete Linkage 332 Average Linkage 333 Centroid Linkage 333 Dendrograms: Displaying Clustering Process and Results 334 Validating Clusters 335 Limitations of Hierarchical Clustering 339 14.5 Nonhierarchical Clustering: The k-Means Algorithm 340 Initial Partition into k Clusters 342 Problems 350 PART VI FORECASTING TIME SERIES CHAPTER 15 Handling Time Series 355 15.1 Introduction 355 15.2 Descriptive vs. Predictive Modeling 356 15.3 Popular Forecasting Methods in Business 357 Combining Methods 357 15.4 Time Series Components 358 Example: Ridership on Amtrak Trains 358 15.5 Data Partitioning and Performance Evaluation 362 Benchmark Performance: Naive Forecasts 362 Generating Future Forecasts 363 Problems 365 CHAPTER 16 Regression-Based Forecasting 368 16.1 A Model with Trend 368 Linear Trend 368 Exponential Trend 372 Polynomial Trend 374 16.2 A Model with Seasonality 375 16.3 A Model with Trend and Seasonality 378 16.4 Autocorrelation and ARIMA Models 378 Computing Autocorrelation 380 Computing Autocorrelation 380 Improving Forecasts by Integrating Autocorrelation Information 383 Improving Forecasts by Integrating Autocorrelation Information383 Fitting AR Models to Residuals 384 Fitting AR Models to Residuals 384 Evaluating Predictability 387 Evaluating Predictability 387 Problems 389 CHAPTER 17 Smoothing Methods 399 17.1 Introduction 399 17.2 Moving Average 400 Centered Moving Average for Visualization 400 Trailing Moving Average for Forecasting 401 Choosing Window Width (w) 404 17.3 Simple Exponential Smoothing 405 Choosing Smoothing Parameter 406 Relation Between Moving Average and Simple Exponential Smoothing 408 17.4 Advanced Exponential Smoothing 409 Series with a trend 409 Series with a Trend and Seasonality 410 Problems 414 PART VII CASES CHAPTER 18 Cases 425 18.1 Charles Book Club 425 18.2 German Credit 434 Background 434 Data 434 18.3 Tayko Software Cataloger 439 18.4 Political Persuasion 442 Background 442 Predictive Analytics Arrives in US Politics 442 Political Targeting 442 Uplift 443 Data 444 Assignment 444 18.5 Taxi Cancellations 446 Business Situation 446 Assignment 446 18.6 Segmenting Consumers of Bath Soap 448 Appendix 451 18.7 Direct-Mail Fundraising 452 18.8 Predicting Bankruptcy 455 18.9 Time Series Case: Forecasting Public Transportation Demand 458 References 460 Data Files Used in the Book 461 Index 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