{"product_id":"machine-learning-for-business-analytics-9781119903833","title":"Machine Learning for Business Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eMACHINE LEARNING FOR BUSINESS ANALYTICS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eAn up-to-date introduction to a market-leading platform for data analysis and machine learning\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro\u003csup\u003e\u003c\/sup\u003e, 2\u003csup\u003end\u003c\/sup\u003e ed.\u003c\/i\u003e offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro\u003csup\u003e\u003c\/sup\u003e, 2\u003csup\u003end\u003c\/sup\u003e ed.\u003c\/i\u003e readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUpdated material which improves the book's usefulness as a reference for professionals beyond the classroom\u003c\/li\u003e \u003cli\u003eFour new chap\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xx\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Preliminaries\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What Is Business Analytics? 3\u003c\/p\u003e \u003cp\u003e1.2 What Is Machine Learning? 5\u003c\/p\u003e \u003cp\u003e1.3 Machine Learning, AI, and Related Terms 5\u003c\/p\u003e \u003cp\u003eStatistical Modeling vs. Machine Learning 6\u003c\/p\u003e \u003cp\u003e1.4 Big Data 6\u003c\/p\u003e \u003cp\u003e1.5 Data Science 7\u003c\/p\u003e \u003cp\u003e1.6 Why Are There So Many Different Methods? 8\u003c\/p\u003e \u003cp\u003e1.7 Terminology and Notation 8\u003c\/p\u003e \u003cp\u003e1.8 Road Maps to This Book 10\u003c\/p\u003e \u003cp\u003eOrder of Topics 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Overview of the Machine Learning Process 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.2 Core Ideas in Machine Learning 18\u003c\/p\u003e \u003cp\u003eClassification 18\u003c\/p\u003e \u003cp\u003ePrediction 18\u003c\/p\u003e \u003cp\u003eAssociation Rules and Recommendation Systems 18\u003c\/p\u003e \u003cp\u003ePredictive Analytics 19\u003c\/p\u003e \u003cp\u003eData Reduction and Dimension Reduction 19\u003c\/p\u003e \u003cp\u003eData Exploration and Visualization 19\u003c\/p\u003e \u003cp\u003eSupervised and Unsupervised Learning 19\u003c\/p\u003e \u003cp\u003e2.3 The Steps in A Machine Learning Project 21\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 22\u003c\/p\u003e \u003cp\u003eOrganization of Data 22\u003c\/p\u003e \u003cp\u003eSampling from a Database 22\u003c\/p\u003e \u003cp\u003eOversampling Rare Events in Classification Tasks 23\u003c\/p\u003e \u003cp\u003ePreprocessing and Cleaning the Data 23\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 29\u003c\/p\u003e \u003cp\u003eOverfitting 29\u003c\/p\u003e \u003cp\u003eCreation and Use of Data Partitions 31\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model with JMP Pro 34\u003c\/p\u003e \u003cp\u003ePredicting Home Values in a Boston Neighborhood 34\u003c\/p\u003e \u003cp\u003eModeling Process 36\u003c\/p\u003e \u003cp\u003e2.7 Using JMP Pro for Machine Learning 42\u003c\/p\u003e \u003cp\u003e2.8 Automating Machine Learning Solutions 43\u003c\/p\u003e \u003cp\u003ePredicting Power Generator Failure 44\u003c\/p\u003e \u003cp\u003eUber’s Michelangelo 45\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Machine Learning 47\u003c\/p\u003e \u003cp\u003eMachine Learning Software: The State of the Market by Herb\u003c\/p\u003e \u003cp\u003eEdelstein 47\u003c\/p\u003e \u003cp\u003eProblems 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Data Exploration and Dimension Reduction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data Visualization 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 59\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 61\u003c\/p\u003e \u003cp\u003eExample 1: Boston Housing Data 61\u003c\/p\u003e \u003cp\u003eExample 2: Ridership on Amtrak Trains 62\u003c\/p\u003e \u003cp\u003e3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62\u003c\/p\u003e \u003cp\u003eDistribution Plots: Boxplots and Histograms 64\u003c\/p\u003e \u003cp\u003eHeatmaps 67\u003c\/p\u003e \u003cp\u003e3.4 Multidimensional Visualization 70\u003c\/p\u003e \u003cp\u003eAdding Variables: Color, Hue, Size, Shape, Multiple Panels,\u003c\/p\u003e \u003cp\u003eAnimation 70\u003c\/p\u003e \u003cp\u003eManipulations: Rescaling, Aggregation and Hierarchies, Zooming,\u003c\/p\u003e \u003cp\u003eFiltering 73\u003c\/p\u003e \u003cp\u003eReference: Trend Line and Labels 77\u003c\/p\u003e \u003cp\u003eScaling Up: Large Datasets 79\u003c\/p\u003e \u003cp\u003eMultivariate Plot: Parallel Coordinates Plot 80\u003c\/p\u003e \u003cp\u003eInteractive Visualization 80\u003c\/p\u003e \u003cp\u003e3.5 Specialized Visualizations 82\u003c\/p\u003e \u003cp\u003eVisualizing Networked Data 82\u003c\/p\u003e \u003cp\u003eVisualizing Hierarchical Data: More on Treemaps 83\u003c\/p\u003e \u003cp\u003eVisualizing Geographical Data: Maps 84\u003c\/p\u003e \u003cp\u003e3.6 Summary: Major Visualizations and Operations, According to\u003c\/p\u003e \u003cp\u003eMachine Learning Goal 87\u003c\/p\u003e \u003cp\u003ePrediction 87\u003c\/p\u003e \u003cp\u003eClassification 87\u003c\/p\u003e \u003cp\u003eTime Series Forecasting 87\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 88\u003c\/p\u003e \u003cp\u003eProblems 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Dimension Reduction 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 91\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 92\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 92\u003c\/p\u003e \u003cp\u003eProblems 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Performance Evaluation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Evaluating Predictive Performance 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 118\u003c\/p\u003e \u003cp\u003e5.2 Evaluating Predictive Performance 118\u003c\/p\u003e \u003cp\u003eProblems 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Prediction and Classification Methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Multiple Linear Regression 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 147\u003c\/p\u003e \u003cp\u003e6.2 Explanatory vs. Predictive Modeling 148\u003c\/p\u003e \u003cp\u003e6.3 Estimating the Regression Equation and Prediction 149\u003c\/p\u003e \u003cp\u003eExample: Predicting the Price of Used Toyota Corolla\u003c\/p\u003e \u003cp\u003eAutomobiles 150\u003c\/p\u003e \u003cp\u003e6.4 Variable Selection in Linear Regression 155\u003c\/p\u003e \u003cp\u003eReducing the Number of Predictors 155\u003c\/p\u003e \u003cp\u003eHow to Reduce the Number of Predictors 156\u003c\/p\u003e \u003cp\u003eManual Variable Selection 156\u003c\/p\u003e \u003cp\u003eAutomated Variable Selection 157\u003c\/p\u003e \u003cp\u003eRegularization (Shriknage Models) 164\u003c\/p\u003e \u003cp\u003eProblems 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 k-Nearest Neighbors (k-NN) 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The 𝑘-NN Classifier (Categorical Outcome) 175\u003c\/p\u003e \u003cp\u003eProblems 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Naive Bayes Classifier 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 189\u003c\/p\u003e \u003cp\u003eThreshold Probability Method 190\u003c\/p\u003e \u003cp\u003eConditional Probability 190\u003c\/p\u003e \u003cp\u003eProblems 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Classification and Regression Trees 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 206\u003c\/p\u003e \u003cp\u003eTree Structure 206\u003c\/p\u003e \u003cp\u003eDecision Rules 207\u003c\/p\u003e \u003cp\u003eClassifying a New Record 207\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 207\u003c\/p\u003e \u003cp\u003eRecursive Partitioning 207\u003c\/p\u003e \u003cp\u003eExample 1: Riding Mowers 208\u003c\/p\u003e \u003cp\u003eCategorical Predictors 210\u003c\/p\u003e \u003cp\u003eStandardization 210\u003c\/p\u003e \u003cp\u003e9.3 Growing a Tree for Riding Mowers Example 210\u003c\/p\u003e \u003cp\u003eChoice of First Split 211\u003c\/p\u003e \u003cp\u003eChoice of Second Split 212\u003c\/p\u003e \u003cp\u003eFinal Tree 212\u003c\/p\u003e \u003cp\u003eUsing a Tree to Classify New Records 213\u003c\/p\u003e \u003cp\u003e9.4 Evaluating the Performance of a Classification Tree 215\u003c\/p\u003e \u003cp\u003eExample 2: Acceptance of Personal Loan 215\u003c\/p\u003e \u003cp\u003e9.5 Avoiding Overfitting 219\u003c\/p\u003e \u003cp\u003eStopping Tree Growth: CHAID 220\u003c\/p\u003e \u003cp\u003eGrowing a Full Tree and Pruning It Back 220\u003c\/p\u003e \u003cp\u003eHow JMP Pro Limits Tree Size 221\u003c\/p\u003e \u003cp\u003e9.6 Classification Rules from Trees 222\u003c\/p\u003e \u003cp\u003e9.7 Classification Trees for More Than Two Classes 224\u003c\/p\u003e \u003cp\u003e9.8 Regression Trees 224\u003c\/p\u003e \u003cp\u003ePrediction 224\u003c\/p\u003e \u003cp\u003eEvaluating Performance 225\u003c\/p\u003e \u003cp\u003e9.9 Advantages and Weaknesses of a Single Tree 227\u003c\/p\u003e \u003cp\u003e9.10 Improving Prediction: Random Forests and Boosted Trees 229\u003c\/p\u003e \u003cp\u003eRandom Forests 229\u003c\/p\u003e \u003cp\u003eBoosted Trees 230\u003c\/p\u003e \u003cp\u003eProblems 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Logistic Regression 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 237\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 239\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 240\u003c\/p\u003e \u003cp\u003eModel with a Single Predictor 241\u003c\/p\u003e \u003cp\u003eEstimating the Logistic Model from Data: Multiple Predictors 243\u003c\/p\u003e \u003cp\u003eInterpreting Results in Terms of Odds (for a Profiling Goal) 246\u003c\/p\u003e \u003cp\u003e10.4 Evaluating Classification Performance 247\u003c\/p\u003e \u003cp\u003e10.5 Variable Selection 249\u003c\/p\u003e \u003cp\u003e10.6 Logistic Regression for Multi-class Classification 250\u003c\/p\u003e \u003cp\u003eLogistic Regression for Nominal Classes 250\u003c\/p\u003e \u003cp\u003eLogistic Regression for Ordinal Classes 251\u003c\/p\u003e \u003cp\u003eExample: Accident Data 252\u003c\/p\u003e \u003cp\u003e10.7 Example of Complete Analysis: Predicting Delayed Flights 253\u003c\/p\u003e \u003cp\u003eData Preprocessing 255\u003c\/p\u003e \u003cp\u003eModel Fitting, Estimation, and Interpretation---A Simple Model 256\u003c\/p\u003e \u003cp\u003eModel Fitting, Estimation and Interpretation---The Full Model 257\u003c\/p\u003e \u003cp\u003eModel Performance 257\u003c\/p\u003e \u003cp\u003eProblems 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Neural Nets 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 267\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 268\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 269\u003c\/p\u003e \u003cp\u003eExample 1: Tiny Dataset 269\u003c\/p\u003e \u003cp\u003eComputing Output of Nodes 269\u003c\/p\u003e \u003cp\u003ePreprocessing the Data 272\u003c\/p\u003e \u003cp\u003eTraining the Model 273\u003c\/p\u003e \u003cp\u003eUsing the Output for Prediction and Classification 279\u003c\/p\u003e \u003cp\u003eExample 2: Classifying Accident Severity 279\u003c\/p\u003e \u003cp\u003eAvoiding Overfitting 281\u003c\/p\u003e \u003cp\u003e11.4 User Input in JMP Pro 282\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Outcome 284\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 285\u003c\/p\u003e \u003cp\u003eConvolutional Neural Networks (CNNs) 285\u003c\/p\u003e \u003cp\u003eLocal Feature Map 287\u003c\/p\u003e \u003cp\u003eA Hierarchy of Features 287\u003c\/p\u003e \u003cp\u003eThe Learning Process 287\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 288\u003c\/p\u003e \u003cp\u003eConclusion 289\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 289\u003c\/p\u003e \u003cp\u003eProblems 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Discriminant Analysis 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 293\u003c\/p\u003e \u003cp\u003eExample 1: Riding Mowers 294\u003c\/p\u003e \u003cp\u003eExample 2: Personal Loan Acceptance 294\u003c\/p\u003e \u003cp\u003e12.2 Distance of an Observation from a Class 295\u003c\/p\u003e \u003cp\u003e12.3 From Distances to Propensities and Classifications 297\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 300\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 301\u003c\/p\u003e \u003cp\u003e12.6 Classifying More Than Two Classes 303\u003c\/p\u003e \u003cp\u003eExample 3: Medical Dispatch to Accident Scenes 303\u003c\/p\u003e \u003cp\u003e12.7 Advantages and Weaknesses 306\u003c\/p\u003e \u003cp\u003eProblems 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Generating, Comparing, and Combining Multiple Models 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Ensembles 311\u003c\/p\u003e \u003cp\u003eWhy Ensembles Can Improve Predictive Power 312\u003c\/p\u003e \u003cp\u003eSimple Averaging or Voting 313\u003c\/p\u003e \u003cp\u003eBagging 314\u003c\/p\u003e \u003cp\u003eBoosting 315\u003c\/p\u003e \u003cp\u003eStacking 316\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Ensembles 317\u003c\/p\u003e \u003cp\u003e13.2 Automated Machine Learning (AutoML) 317\u003c\/p\u003e \u003cp\u003eAutoML: Explore and Clean Data 317\u003c\/p\u003e \u003cp\u003eAutoML: Determine Machine Learning Task 318\u003c\/p\u003e \u003cp\u003eAutoML: Choose Features and Machine Learning Methods 318\u003c\/p\u003e \u003cp\u003eAutoML: Evaluate Model Performance 320\u003c\/p\u003e \u003cp\u003eAutoML: Model Deployment 321\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Automated Machine Learning 322\u003c\/p\u003e \u003cp\u003e13.3 Summary 322\u003c\/p\u003e \u003cp\u003eProblems 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Intervention and User Feedback\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 327\u003c\/p\u003e \u003cp\u003e14.2 A\/B Testing 328\u003c\/p\u003e \u003cp\u003eExample: Testing a New Feature in a Photo Sharing App 329\u003c\/p\u003e \u003cp\u003eThe Statistical Test for Comparing Two Groups (𝑇 -Test) 329\u003c\/p\u003e \u003cp\u003eMultiple Treatment Groups: A\/B\/n Tests 333\u003c\/p\u003e \u003cp\u003eMultiple A\/B Tests and the Danger of Multiple Testing 333\u003c\/p\u003e \u003cp\u003e14.3 Uplift (Persuasion) Modeling 333\u003c\/p\u003e \u003cp\u003eGetting the Data 334\u003c\/p\u003e \u003cp\u003eA Simple Model 336\u003c\/p\u003e \u003cp\u003eModeling Individual Uplift 336\u003c\/p\u003e \u003cp\u003eCreating Uplift Models in JMP Pro 337\u003c\/p\u003e \u003cp\u003eUsing the Results of an Uplift Model 338\u003c\/p\u003e \u003cp\u003e14.4 Reinforcement Learning 340\u003c\/p\u003e \u003cp\u003eExplore-Exploit: Multi-armed Bandits 340\u003c\/p\u003e \u003cp\u003eMarkov Decision Process (MDP) 341\u003c\/p\u003e \u003cp\u003e14.5 Summary 344\u003c\/p\u003e \u003cp\u003eProblems 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Mining Relationships Among Records\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Association Rules and Collaborative Filtering 349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Association Rules 349\u003c\/p\u003e \u003cp\u003eDiscovering Association Rules in Transaction Databases 350\u003c\/p\u003e \u003cp\u003eExample 1: Synthetic Data on Purchases of Phone Faceplates 350\u003c\/p\u003e \u003cp\u003eData Format 350\u003c\/p\u003e \u003cp\u003eGenerating Candidate Rules 352\u003c\/p\u003e \u003cp\u003eThe Apriori Algorithm 353\u003c\/p\u003e \u003cp\u003eSelecting Strong Rules 353\u003c\/p\u003e \u003cp\u003eThe Process of Rule Selection 356\u003c\/p\u003e \u003cp\u003eInterpreting the Results 358\u003c\/p\u003e \u003cp\u003eRules and Chance 359\u003c\/p\u003e \u003cp\u003eExample 2: Rules for Similar Book Purchases 361\u003c\/p\u003e \u003cp\u003e15.2 Collaborative Filtering 362\u003c\/p\u003e \u003cp\u003eData Type and Format 363\u003c\/p\u003e \u003cp\u003eExample 3: Netflix Prize Contest 363\u003c\/p\u003e \u003cp\u003eUser-Based Collaborative Filtering: “People Like You” 365\u003c\/p\u003e \u003cp\u003eItem-Based Collaborative Filtering 366\u003c\/p\u003e \u003cp\u003eEvaluating Performance 367\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Collaborative Filtering 368\u003c\/p\u003e \u003cp\u003eCollaborative Filtering vs. Association Rules 369\u003c\/p\u003e \u003cp\u003e15.3 Summary 370\u003c\/p\u003e \u003cp\u003eProblems 372\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Cluster Analysis 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 375\u003c\/p\u003e \u003cp\u003eExample: Public Utilities 377\u003c\/p\u003e \u003cp\u003e16.2 Measuring Distance Between Two Records 378\u003c\/p\u003e \u003cp\u003eEuclidean Distance 379\u003c\/p\u003e \u003cp\u003eStandardizing Numerical Measurements 379\u003c\/p\u003e \u003cp\u003eOther Distance Measures for Numerical Data 379\u003c\/p\u003e \u003cp\u003eDistance Measures for Categorical Data 382\u003c\/p\u003e \u003cp\u003eDistance Measures for Mixed Data 382\u003c\/p\u003e \u003cp\u003e16.3 Measuring Distance Between Two Clusters 383\u003c\/p\u003e \u003cp\u003eMinimum Distance 383\u003c\/p\u003e \u003cp\u003eMaximum Distance 383\u003c\/p\u003e \u003cp\u003eAverage Distance 383\u003c\/p\u003e \u003cp\u003eCentroid Distance 383\u003c\/p\u003e \u003cp\u003e16.4 Hierarchical (Agglomerative) Clustering 385\u003c\/p\u003e \u003cp\u003eSingle Linkage 385\u003c\/p\u003e \u003cp\u003eComplete Linkage 386\u003c\/p\u003e \u003cp\u003eAverage Linkage 386\u003c\/p\u003e \u003cp\u003eCentroid Linkage 386\u003c\/p\u003e \u003cp\u003eWard’s Method 387\u003c\/p\u003e \u003cp\u003eDendrograms: Displaying Clustering Process and Results 387\u003c\/p\u003e \u003cp\u003eValidating Clusters 391\u003c\/p\u003e \u003cp\u003eTwo-Way Clustering 393\u003c\/p\u003e \u003cp\u003eLimitations of Hierarchical Clustering 393\u003c\/p\u003e \u003cp\u003e16.5 Nonhierarchical Clustering: The 𝐾-Means Algorithm 394\u003c\/p\u003e \u003cp\u003eChoosing the Number of Clusters (𝑘) 396\u003c\/p\u003e \u003cp\u003eProblems 403\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII Forecasting Time Series\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Handling Time Series 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 409\u003c\/p\u003e \u003cp\u003e17.2 Descriptive vs. Predictive Modeling 410\u003c\/p\u003e \u003cp\u003e17.3 Popular Forecasting Methods in Business 411\u003c\/p\u003e \u003cp\u003eCombining Methods 411\u003c\/p\u003e \u003cp\u003e17.4 Time Series Components 411\u003c\/p\u003e \u003cp\u003eExample: Ridership on Amtrak Trains 412\u003c\/p\u003e \u003cp\u003e17.5 Data Partitioning and Performance Evaluation 415\u003c\/p\u003e \u003cp\u003eBenchmark Performance: Naive Forecasts 417\u003c\/p\u003e \u003cp\u003eGenerating Future Forecasts 417\u003c\/p\u003e \u003cp\u003eProblems 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Regression-Based Forecasting 423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 A Model with Trend 424\u003c\/p\u003e \u003cp\u003eLinear Trend 424\u003c\/p\u003e \u003cp\u003eExponential Trend 427\u003c\/p\u003e \u003cp\u003ePolynomial Trend 429\u003c\/p\u003e \u003cp\u003e18.2 A Model with Seasonality 430\u003c\/p\u003e \u003cp\u003eAdditive vs. Multiplicative Seasonality 432\u003c\/p\u003e \u003cp\u003e18.3 A Model with Trend and Seasonality 433\u003c\/p\u003e \u003cp\u003e18.4 Autocorrelation and ARIMA Models 433\u003c\/p\u003e \u003cp\u003eComputing Autocorrelation 433\u003c\/p\u003e \u003cp\u003eImproving Forecasts by Integrating Autocorrelation Information 437\u003c\/p\u003e \u003cp\u003eFitting AR Models to Residuals 439\u003c\/p\u003e \u003cp\u003eEvaluating Predictability 441\u003c\/p\u003e \u003cp\u003eProblems 444\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Smoothing and Deep Learning Methods for Forecasting 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 455\u003c\/p\u003e \u003cp\u003e19.2 Moving Average 456\u003c\/p\u003e \u003cp\u003eCentered Moving Average for Visualization 456\u003c\/p\u003e \u003cp\u003eTrailing Moving Average for Forecasting 457\u003c\/p\u003e \u003cp\u003eChoosing Window Width (𝑤) 460\u003c\/p\u003e \u003cp\u003e19.3 Simple Exponential Smoothing 461\u003c\/p\u003e \u003cp\u003eChoosing Smoothing Parameter 𝛼 462\u003c\/p\u003e \u003cp\u003eRelation Between Moving Average and Simple Exponential\u003c\/p\u003e \u003cp\u003eSmoothing 465\u003c\/p\u003e \u003cp\u003e19.4 Advanced Exponential Smoothing 465\u003c\/p\u003e \u003cp\u003eSeries With a Trend 465\u003c\/p\u003e \u003cp\u003eSeries With a Trend and Seasonality 466\u003c\/p\u003e \u003cp\u003e19.5 Deep Learning for Forecasting 470\u003c\/p\u003e \u003cp\u003eProblems 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Text Mining 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 483\u003c\/p\u003e \u003cp\u003e20.2 The Tabular Representation of Text: Document–Term Matrix and\u003c\/p\u003e \u003cp\u003e“Bag-of-Words” 484\u003c\/p\u003e \u003cp\u003e20.3 Bag-of-Words vs. Meaning Extraction at Document Level 486\u003c\/p\u003e \u003cp\u003e20.4 Preprocessing the Text 486\u003c\/p\u003e \u003cp\u003eTokenization 487\u003c\/p\u003e \u003cp\u003eText Reduction 488\u003c\/p\u003e \u003cp\u003ePresence\/Absence vs. Frequency (Occurrences) 489\u003c\/p\u003e \u003cp\u003eTerm Frequency-Inverse Document Frequency (TF-IDF) 489\u003c\/p\u003e \u003cp\u003eFrom Terms to Topics: Latent Semantic Analysis and Topic\u003c\/p\u003e \u003cp\u003eAnalysis 490\u003c\/p\u003e \u003cp\u003eExtracting Meaning 491\u003c\/p\u003e \u003cp\u003eFrom Terms to High Dimensional Word Vectors: Word2Vec 491\u003c\/p\u003e \u003cp\u003e20.5 Implementing Machine Learning Methods 492\u003c\/p\u003e \u003cp\u003e20.6 Example: Online Discussions on Autos and Electronics 492\u003c\/p\u003e \u003cp\u003eImporting the Records 493\u003c\/p\u003e \u003cp\u003eText Preprocessing in JMP 494\u003c\/p\u003e \u003cp\u003eUsing Latent Semantic Analysis and Topic Analysis 496\u003c\/p\u003e \u003cp\u003eFitting a Predictive Model 499\u003c\/p\u003e \u003cp\u003ePrediction 499\u003c\/p\u003e \u003cp\u003e20.7 Example: Sentiment Analysis of Movie Reviews 500\u003c\/p\u003e \u003cp\u003eData Preparation 500\u003c\/p\u003e \u003cp\u003eLatent Semantic Analysis and Fitting a Predictive Model 500\u003c\/p\u003e \u003cp\u003e20.8 Summary 502\u003c\/p\u003e \u003cp\u003eProblems 503\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Responsible Data Science 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 505\u003c\/p\u003e \u003cp\u003eExample: Predicting Recidivism 506\u003c\/p\u003e \u003cp\u003e21.2 Unintentional Harm 506\u003c\/p\u003e \u003cp\u003e21.3 Legal Considerations 508\u003c\/p\u003e \u003cp\u003eThe General Data Protection Regulation (GDPR) 508\u003c\/p\u003e \u003cp\u003eProtected Groups 508\u003c\/p\u003e \u003cp\u003e21.4 Principles of Responsible Data Science 508\u003c\/p\u003e \u003cp\u003eNon-maleficence 509\u003c\/p\u003e \u003cp\u003eFairness 509\u003c\/p\u003e \u003cp\u003eTransparency 510\u003c\/p\u003e \u003cp\u003eAccountability 511\u003c\/p\u003e \u003cp\u003eData Privacy and Security 511\u003c\/p\u003e \u003cp\u003e21.5 A Responsible Data Science Framework 511\u003c\/p\u003e \u003cp\u003eJustification 511\u003c\/p\u003e \u003cp\u003eAssembly 512\u003c\/p\u003e \u003cp\u003eData Preparation 513\u003c\/p\u003e \u003cp\u003eModeling 513\u003c\/p\u003e \u003cp\u003eAuditing 513\u003c\/p\u003e \u003cp\u003e21.6 Documentation Tools 514\u003c\/p\u003e \u003cp\u003eImpact Statements 514\u003c\/p\u003e \u003cp\u003eModel Cards 515\u003c\/p\u003e \u003cp\u003eDatasheets 516\u003c\/p\u003e \u003cp\u003eAudit Reports 516\u003c\/p\u003e \u003cp\u003e21.7 Example: Applying the RDS Framework to the COMPAS Example 517\u003c\/p\u003e \u003cp\u003eUnanticipated Uses 518\u003c\/p\u003e \u003cp\u003eEthical Concerns 518\u003c\/p\u003e \u003cp\u003eProtected Groups 518\u003c\/p\u003e \u003cp\u003eData Issues 518\u003c\/p\u003e \u003cp\u003eFitting the Model 519\u003c\/p\u003e \u003cp\u003eAuditing the Model 520\u003c\/p\u003e \u003cp\u003eBias Mitigation 526\u003c\/p\u003e \u003cp\u003e21.8 Summary 526\u003c\/p\u003e \u003cp\u003eProblems 528\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IX Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Cases 533\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Charles Book Club 533\u003c\/p\u003e \u003cp\u003eThe Book Industry 533\u003c\/p\u003e \u003cp\u003eDatabase Marketing at Charles 534\u003c\/p\u003e \u003cp\u003eMachine Learning Techniques 535\u003c\/p\u003e \u003cp\u003eAssignment 537\u003c\/p\u003e \u003cp\u003e22.2 German Credit 541\u003c\/p\u003e \u003cp\u003eBackground 541\u003c\/p\u003e \u003cp\u003eData 541\u003c\/p\u003e \u003cp\u003eAssignment 544\u003c\/p\u003e \u003cp\u003e22.3 Tayko Software Cataloger 545\u003c\/p\u003e \u003cp\u003eBackground 545\u003c\/p\u003e \u003cp\u003eThe Mailing Experiment 545\u003c\/p\u003e \u003cp\u003eData 545\u003c\/p\u003e \u003cp\u003eAssignment 546\u003c\/p\u003e \u003cp\u003e22.4 Political Persuasion 548\u003c\/p\u003e \u003cp\u003eBackground 548\u003c\/p\u003e \u003cp\u003ePredictive Analytics Arrives in US Politics 548\u003c\/p\u003e \u003cp\u003ePolitical Targeting 548\u003c\/p\u003e \u003cp\u003eUplift 549\u003c\/p\u003e \u003cp\u003eData 549\u003c\/p\u003e \u003cp\u003eAssignment 550\u003c\/p\u003e \u003cp\u003e22.5 Taxi Cancellations 552\u003c\/p\u003e \u003cp\u003eBusiness Situation 552\u003c\/p\u003e \u003cp\u003eAssignment 552\u003c\/p\u003e \u003cp\u003e22.6 Segmenting Consumers of Bath Soap 554\u003c\/p\u003e \u003cp\u003eBusiness Situation 554\u003c\/p\u003e \u003cp\u003eKey Problems 554\u003c\/p\u003e \u003cp\u003eData 555\u003c\/p\u003e \u003cp\u003eMeasuring Brand Loyalty 556\u003c\/p\u003e \u003cp\u003eAssignment 556\u003c\/p\u003e \u003cp\u003e22.7 Catalog Cross-Selling 557\u003c\/p\u003e \u003cp\u003eBackground 557\u003c\/p\u003e \u003cp\u003eAssignment 557\u003c\/p\u003e \u003cp\u003e22.8 Direct-Mail Fundraising 559\u003c\/p\u003e \u003cp\u003eBackground 559\u003c\/p\u003e \u003cp\u003eData 559\u003c\/p\u003e \u003cp\u003eAssignment 559\u003c\/p\u003e \u003cp\u003e22.9 Time Series Case: Forecasting Public Transportation Demand 562\u003c\/p\u003e \u003cp\u003eBackground 562\u003c\/p\u003e \u003cp\u003eProblem Description 562\u003c\/p\u003e \u003cp\u003eAvailable Data 562\u003c\/p\u003e \u003cp\u003eAssignment Goal 562\u003c\/p\u003e \u003cp\u003eAssignment 563\u003c\/p\u003e \u003cp\u003eTips and Suggested Steps 563\u003c\/p\u003e \u003cp\u003e22.10 Loan Approval 564\u003c\/p\u003e \u003cp\u003eBackground 564\u003c\/p\u003e \u003cp\u003eRegulatory Requirements 564\u003c\/p\u003e \u003cp\u003eGetting Started 564\u003c\/p\u003e \u003cp\u003eAssignment 564\u003c\/p\u003e \u003cp\u003eReferences 567\u003c\/p\u003e \u003cp\u003eData Files Used in the Book 571\u003c\/p\u003e \u003cp\u003eIndex 573\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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