Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro

Data Mining for Business Analytics with JMP Pro® hits the sweet spot in terms of balancing the technical and applied aspects of data mining.  The content and technical level of the book works beautifully for a variety of students ranging from undergraduates to MBAs to those in applied graduate programs. –        Allison Jones–Farmer, Van Andel Professor of Business Analytics & Director of the Center for Analytics and Data Science, Department of Information Systems & Analytics, Farmer School of Business, Miami University Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands–on applications with JMP Pro®, 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, naïve Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. 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, exercise and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for upper–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.  INDICE: 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 463

• ISBN: 978-1-118-87743-2
• Editorial: Wiley–Blackwell
• Encuadernacion: Cartoné
• Páginas: 464
• Fecha Publicación: 15/06/2016
• Nº Volúmenes: 1
• Idioma: Inglés