{"product_id":"machine-learning-for-business-analytics-isbn-9781119835172","title":"Machine Learning for Business Analytics","description":"\u003cp\u003e\u003cb\u003eMACHINE LEARNING FOR BUSINESS ANALYTICS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMachine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications in R\u003c\/i\u003e provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.\u003c\/p\u003e \u003cp\u003eThis is the second R edition of \u003ci\u003eMachine Learning for Business Analytics\u003c\/i\u003e. This edition also includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R\u003c\/li\u003e \u003cli\u003eAn expanded chapter focused on discussion of deep learning techniques\u003c\/li\u003e \u003cli\u003eA new chapter on experimental feedback techniques including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e \u003cli\u003eA new chapter on responsible data science\u003c\/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003eA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques\u003c\/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.\u003c\/p\u003e \u003cp\u003eForeword by \u003ci\u003eRavi Bapna\u003c\/i\u003e xix\u003c\/p\u003e \u003cp\u003eForeword by \u003ci\u003eGareth James\u003c\/i\u003e xxi\u003c\/p\u003e \u003cp\u003ePreface to the Second R Edition xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Preliminaries \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 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\u003e1.4 Big Data 7\u003c\/p\u003e \u003cp\u003e1.5 Data Science 8\u003c\/p\u003e \u003cp\u003e1.6 Why Are There So Many Different Methods? 8\u003c\/p\u003e \u003cp\u003e1.7 Terminology and Notation 9\u003c\/p\u003e \u003cp\u003e1.8 Road Maps to This Book 11\u003c\/p\u003e \u003cp\u003eOrder of Topics 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 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 20\u003c\/p\u003e \u003cp\u003e2.3 The Steps in a Machine Learning Project 21\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 23\u003c\/p\u003e \u003cp\u003eOrganization of Data 23\u003c\/p\u003e \u003cp\u003ePredicting Home Values in the West Roxbury Neighborhood 23\u003c\/p\u003e \u003cp\u003eLoading and Looking at the Data in R 24\u003c\/p\u003e \u003cp\u003eSampling from a Database 26\u003c\/p\u003e \u003cp\u003eOversampling Rare Events in Classification Tasks 27\u003c\/p\u003e \u003cp\u003ePreprocessing and Cleaning the Data 28\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 35\u003c\/p\u003e \u003cp\u003eOverfitting 36\u003c\/p\u003e \u003cp\u003eCreating and Using Data Partitions 38\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model 41\u003c\/p\u003e \u003cp\u003eModeling Process 41\u003c\/p\u003e \u003cp\u003e2.7 Using R for Machine Learning on a Local Machine 46\u003c\/p\u003e \u003cp\u003e2.8 Automating Machine Learning Solutions 47\u003c\/p\u003e \u003cp\u003ePredicting Power Generator Failure 48\u003c\/p\u003e \u003cp\u003eUber’s Michelangelo 50\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Machine Learning 52\u003c\/p\u003e \u003cp\u003eMachine Learning Software: The State of the Market (by Herb Edelstein) 53\u003c\/p\u003e \u003cp\u003eProblems 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Data Exploration and Dimension Reduction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Data Visualization 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Uses of Data Visualization 63\u003c\/p\u003e \u003cp\u003eBase R or ggplot? 65\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 65\u003c\/p\u003e \u003cp\u003eExample 1: Boston Housing Data 65\u003c\/p\u003e \u003cp\u003eExample 2: Ridership on Amtrak Trains 67\u003c\/p\u003e \u003cp\u003e3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 67\u003c\/p\u003e \u003cp\u003eDistribution Plots: Boxplots and Histograms 70\u003c\/p\u003e \u003cp\u003eHeatmaps: Visualizing Correlations and Missing Values 73\u003c\/p\u003e \u003cp\u003e3.4 Multidimensional Visualization 75\u003c\/p\u003e \u003cp\u003eAdding Variables: Color, Size, Shape, Multiple Panels, and Animation 76\u003c\/p\u003e \u003cp\u003eManipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering 79\u003c\/p\u003e \u003cp\u003eReference: Trend Lines and Labels 83\u003c\/p\u003e \u003cp\u003eScaling Up to Large Datasets 85\u003c\/p\u003e \u003cp\u003eMultivariate Plot: Parallel Coordinates Plot 85\u003c\/p\u003e \u003cp\u003eInteractive Visualization 88\u003c\/p\u003e \u003cp\u003e3.5 Specialized Visualizations 91\u003c\/p\u003e \u003cp\u003eVisualizing Networked Data 91\u003c\/p\u003e \u003cp\u003eVisualizing Hierarchical Data: Treemaps 93\u003c\/p\u003e \u003cp\u003eVisualizing Geographical Data: Map Charts 95\u003c\/p\u003e \u003cp\u003e3.6 Major Visualizations and Operations, by Machine Learning Goal 97\u003c\/p\u003e \u003cp\u003ePrediction 97\u003c\/p\u003e \u003cp\u003eClassification 97\u003c\/p\u003e \u003cp\u003eTime Series Forecasting 97\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 98\u003c\/p\u003e \u003cp\u003eProblems 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Dimension Reduction 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 101\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 102\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 102\u003c\/p\u003e \u003cp\u003eExample 1: House Prices in Boston 103\u003c\/p\u003e \u003cp\u003e4.4 Data Summaries 103\u003c\/p\u003e \u003cp\u003eSummary Statistics 104\u003c\/p\u003e \u003cp\u003eAggregation and Pivot Tables 104\u003c\/p\u003e \u003cp\u003e4.5 Correlation Analysis 107\u003c\/p\u003e \u003cp\u003e4.6 Reducing the Number of Categories in Categorical Variables 109\u003c\/p\u003e \u003cp\u003e4.7 Converting a Categorical Variable to a Numerical Variable 111\u003c\/p\u003e \u003cp\u003e4.8 Principal Component Analysis 111\u003c\/p\u003e \u003cp\u003eExample 2: Breakfast Cereals 111\u003c\/p\u003e \u003cp\u003ePrincipal Components 116\u003c\/p\u003e \u003cp\u003eNormalizing the Data 117\u003c\/p\u003e \u003cp\u003eUsing Principal Components for Classification and Prediction 120\u003c\/p\u003e \u003cp\u003e4.9 Dimension Reduction Using Regression Models 121\u003c\/p\u003e \u003cp\u003e4.10 Dimension Reduction Using Classification and Regression Trees 121\u003c\/p\u003e \u003cp\u003eProblems 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Performance Evaluation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Evaluating Predictive Performance 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 130\u003c\/p\u003e \u003cp\u003e5.2 Evaluating Predictive Performance 130\u003c\/p\u003e \u003cp\u003eNaive Benchmark: The Average 131\u003c\/p\u003e \u003cp\u003ePrediction Accuracy Measures 131\u003c\/p\u003e \u003cp\u003eComparing Training and Holdout Performance 133\u003c\/p\u003e \u003cp\u003eCumulative Gains and Lift Charts 133\u003c\/p\u003e \u003cp\u003e5.3 Judging Classifier Performance 136\u003c\/p\u003e \u003cp\u003eBenchmark: The Naive Rule 136\u003c\/p\u003e \u003cp\u003eClass Separation 136\u003c\/p\u003e \u003cp\u003eThe Confusion (Classification) Matrix 137\u003c\/p\u003e \u003cp\u003eUsing the Holdout Data 138\u003c\/p\u003e \u003cp\u003eAccuracy Measures 139\u003c\/p\u003e \u003cp\u003ePropensities and Threshold for Classification 139\u003c\/p\u003e \u003cp\u003ePerformance in Case of Unequal Importance of Classes 143\u003c\/p\u003e \u003cp\u003eAsymmetric Misclassification Costs 146\u003c\/p\u003e \u003cp\u003eGeneralization to More Than Two Classes 149\u003c\/p\u003e \u003cp\u003e5.4 Judging Ranking Performance 150\u003c\/p\u003e \u003cp\u003eCumulative Gains and Lift Charts for Binary Data 150\u003c\/p\u003e \u003cp\u003eDecile-wise Lift Charts 153\u003c\/p\u003e \u003cp\u003eBeyond Two Classes 154\u003c\/p\u003e \u003cp\u003eGains and Lift Charts Incorporating Costs and Benefits 154\u003c\/p\u003e \u003cp\u003eCumulative Gains as a Function of Threshold 155\u003c\/p\u003e \u003cp\u003e5.5 Oversampling 156\u003c\/p\u003e \u003cp\u003eCreating an Over-sampled Training Set 158\u003c\/p\u003e \u003cp\u003eEvaluating Model Performance Using a Non-oversampled Holdout Set 159\u003c\/p\u003e \u003cp\u003eEvaluating Model Performance If Only Oversampled Holdout Set Exists 159\u003c\/p\u003e \u003cp\u003eProblems 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Prediction and Classification Methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Multiple Linear Regression 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 167\u003c\/p\u003e \u003cp\u003e6.2 Explanatory vs. Predictive Modeling 168\u003c\/p\u003e \u003cp\u003e6.3 Estimating the Regression Equation and Prediction 170\u003c\/p\u003e \u003cp\u003eExample: Predicting the Price of Used Toyota Corolla Cars 171\u003c\/p\u003e \u003cp\u003eCross-validation and caret 175\u003c\/p\u003e \u003cp\u003e6.4 Variable Selection in Linear Regression 176\u003c\/p\u003e \u003cp\u003eReducing the Number of Predictors 176\u003c\/p\u003e \u003cp\u003eHow to Reduce the Number of Predictors 178\u003c\/p\u003e \u003cp\u003eRegularization (Shrinkage Models) 183\u003c\/p\u003e \u003cp\u003eProblems 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 k-Nearest Neighbors (kNN) 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The k-NN Classifier (Categorical Outcome) 193\u003c\/p\u003e \u003cp\u003eDetermining Neighbors 194\u003c\/p\u003e \u003cp\u003eClassification Rule 194\u003c\/p\u003e \u003cp\u003eExample: Riding Mowers 195\u003c\/p\u003e \u003cp\u003eChoosing k 196\u003c\/p\u003e \u003cp\u003eWeighted k-NN 199\u003c\/p\u003e \u003cp\u003eSetting the Cutoff Value 200\u003c\/p\u003e \u003cp\u003ek-NN with More Than Two Classes 201\u003c\/p\u003e \u003cp\u003eConverting Categorical Variables to Binary Dummies 201\u003c\/p\u003e \u003cp\u003e7.2 k-NN for a Numerical Outcome 201\u003c\/p\u003e \u003cp\u003e7.3 Advantages and Shortcomings of k-NN Algorithms 204\u003c\/p\u003e \u003cp\u003eProblems 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 The Naive Bayes Classifier 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 207\u003c\/p\u003e \u003cp\u003eThreshold Probability Method 208\u003c\/p\u003e \u003cp\u003eConditional Probability 208\u003c\/p\u003e \u003cp\u003eExample 1: Predicting Fraudulent Financial Reporting 208\u003c\/p\u003e \u003cp\u003e8.2 Applying the Full (Exact) Bayesian Classifier 209\u003c\/p\u003e \u003cp\u003eUsing the “Assign to the Most Probable Class” Method 210\u003c\/p\u003e \u003cp\u003eUsing the Threshold Probability Method 210\u003c\/p\u003e \u003cp\u003ePractical Difficulty with the Complete (Exact) Bayes Procedure 210\u003c\/p\u003e \u003cp\u003e8.3 Solution: Naive Bayes 211\u003c\/p\u003e \u003cp\u003eThe Naive Bayes Assumption of Conditional Independence 212\u003c\/p\u003e \u003cp\u003eUsing the Threshold Probability Method 212\u003c\/p\u003e \u003cp\u003eExample 2: Predicting Fraudulent Financial Reports, Two Predictors 213\u003c\/p\u003e \u003cp\u003eExample 3: Predicting Delayed Flights 214\u003c\/p\u003e \u003cp\u003eWorking with Continuous Predictors 218\u003c\/p\u003e \u003cp\u003e8.4 Advantages and Shortcomings of the Naive Bayes Classifier 220\u003c\/p\u003e \u003cp\u003eProblems 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Classification and Regression Trees 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 226\u003c\/p\u003e \u003cp\u003eTree Structure 227\u003c\/p\u003e \u003cp\u003eDecision Rules 227\u003c\/p\u003e \u003cp\u003eClassifying a New Record 227\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 228\u003c\/p\u003e \u003cp\u003eRecursive Partitioning 228\u003c\/p\u003e \u003cp\u003eExample 1: Riding Mowers 228\u003c\/p\u003e \u003cp\u003eMeasures of Impurity 231\u003c\/p\u003e \u003cp\u003e9.3 Evaluating the Performance of a Classification Tree 235\u003c\/p\u003e \u003cp\u003eExample 2: Acceptance of Personal Loan 236\u003c\/p\u003e \u003cp\u003e9.4 Avoiding Overfitting 239\u003c\/p\u003e \u003cp\u003eStopping Tree Growth 242\u003c\/p\u003e \u003cp\u003ePruning the Tree 243\u003c\/p\u003e \u003cp\u003eBest-Pruned Tree 245\u003c\/p\u003e \u003cp\u003e9.5 Classification Rules from Trees 247\u003c\/p\u003e \u003cp\u003e9.6 Classification Trees for More Than Two Classes 248\u003c\/p\u003e \u003cp\u003e9.7 Regression Trees 249\u003c\/p\u003e \u003cp\u003ePrediction 250\u003c\/p\u003e \u003cp\u003eMeasuring Impurity 250\u003c\/p\u003e \u003cp\u003eEvaluating Performance 250\u003c\/p\u003e \u003cp\u003e9.8 Advantages and Weaknesses of a Tree 250\u003c\/p\u003e \u003cp\u003e9.9 Improving Prediction: Random Forests and Boosted Trees 252\u003c\/p\u003e \u003cp\u003eRandom Forests 252\u003c\/p\u003e \u003cp\u003eBoosted Trees 254\u003c\/p\u003e \u003cp\u003eProblems 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Logistic Regression 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 261\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 263\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 264\u003c\/p\u003e \u003cp\u003eModel with a Single Predictor 265\u003c\/p\u003e \u003cp\u003eEstimating the Logistic Model from Data: Computing Parameter Estimates 267\u003c\/p\u003e \u003cp\u003eInterpreting Results in Terms of Odds (for a Profiling Goal) 270\u003c\/p\u003e \u003cp\u003e10.4 Evaluating Classification Performance 271\u003c\/p\u003e \u003cp\u003e10.5 Variable Selection 273\u003c\/p\u003e \u003cp\u003e10.6 Logistic Regression for Multi-Class Classification 274\u003c\/p\u003e \u003cp\u003eOrdinal Classes 275\u003c\/p\u003e \u003cp\u003eNominal Classes 276\u003c\/p\u003e \u003cp\u003e10.7 Example of Complete Analysis: Predicting Delayed Flights 277\u003c\/p\u003e \u003cp\u003eData Preprocessing 282\u003c\/p\u003e \u003cp\u003eModel-Fitting and Estimation 282\u003c\/p\u003e \u003cp\u003eModel Interpretation 282\u003c\/p\u003e \u003cp\u003eModel Performance 284\u003c\/p\u003e \u003cp\u003eVariable Selection 285\u003c\/p\u003e \u003cp\u003eProblems 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Neural Nets 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 293\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 294\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 295\u003c\/p\u003e \u003cp\u003eExample 1: Tiny Dataset 295\u003c\/p\u003e \u003cp\u003eComputing Output of Nodes 296\u003c\/p\u003e \u003cp\u003ePreprocessing the Data 299\u003c\/p\u003e \u003cp\u003eTraining the Model 300\u003c\/p\u003e \u003cp\u003eExample 2: Classifying Accident Severity 304\u003c\/p\u003e \u003cp\u003eAvoiding Overfitting 305\u003c\/p\u003e \u003cp\u003eUsing the Output for Prediction and Classification 305\u003c\/p\u003e \u003cp\u003e11.4 Required User Input 307\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Outcome 308\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 309\u003c\/p\u003e \u003cp\u003eConvolutional Neural Networks (CNNs) 310\u003c\/p\u003e \u003cp\u003eLocal Feature Map 311\u003c\/p\u003e \u003cp\u003eA Hierarchy of Features 311\u003c\/p\u003e \u003cp\u003eThe Learning Process 312\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 312\u003c\/p\u003e \u003cp\u003eExample: Classification of Fashion Images 313\u003c\/p\u003e \u003cp\u003eConclusion 320\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 320\u003c\/p\u003e \u003cp\u003eProblems 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Discriminant Analysis 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 325\u003c\/p\u003e \u003cp\u003eExample 1: Riding Mowers 326\u003c\/p\u003e \u003cp\u003eExample 2: Personal Loan Acceptance 327\u003c\/p\u003e \u003cp\u003e12.2 Distance of a Record from a Class 327\u003c\/p\u003e \u003cp\u003e12.3 Fisher’s Linear Classification Functions 329\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 333\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 334\u003c\/p\u003e \u003cp\u003e12.6 Unequal Misclassification Costs 334\u003c\/p\u003e \u003cp\u003e12.7 Classifying More Than Two Classes 336\u003c\/p\u003e \u003cp\u003eExample 3: Medical Dispatch to Accident Scenes 336\u003c\/p\u003e \u003cp\u003e12.8 Advantages and Weaknesses 339\u003c\/p\u003e \u003cp\u003eProblems 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Generating, Comparing, and Combining Multiple Models 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Ensembles 346\u003c\/p\u003e \u003cp\u003eWhy Ensembles Can Improve Predictive Power 346\u003c\/p\u003e \u003cp\u003eSimple Averaging or Voting 348\u003c\/p\u003e \u003cp\u003eBagging 349\u003c\/p\u003e \u003cp\u003eBoosting 349\u003c\/p\u003e \u003cp\u003eBagging and Boosting in R 349\u003c\/p\u003e \u003cp\u003eStacking 350\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Ensembles 351\u003c\/p\u003e \u003cp\u003e13.2 Automated Machine Learning (AutoML) 352\u003c\/p\u003e \u003cp\u003eAutoML: Explore and Clean Data 352\u003c\/p\u003e \u003cp\u003eAutoML: Determine Machine Learning Task 353\u003c\/p\u003e \u003cp\u003eAutoML: Choose Features and Machine Learning Methods 354\u003c\/p\u003e \u003cp\u003eAutoML: Evaluate Model Performance 354\u003c\/p\u003e \u003cp\u003eAutoML: Model Deployment 356\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Automated Machine Learning 357\u003c\/p\u003e \u003cp\u003e13.3 Explaining Model Predictions 358\u003c\/p\u003e \u003cp\u003e13.4 Summary 360\u003c\/p\u003e \u003cp\u003eProblems 362\u003c\/p\u003e \u003cp\u003e345\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Intervention and User Feedback\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A\/B Testing 368\u003c\/p\u003e \u003cp\u003eExample: Testing a New Feature in a Photo Sharing App 369\u003c\/p\u003e \u003cp\u003eThe Statistical Test for Comparing Two Groups (T-Test) 370\u003c\/p\u003e \u003cp\u003eMultiple Treatment Groups: A\/B\/n Tests 372\u003c\/p\u003e \u003cp\u003eMultiple A\/B Tests and the Danger of Multiple Testing 372\u003c\/p\u003e \u003cp\u003e14.2 Uplift (Persuasion) Modeling 373\u003c\/p\u003e \u003cp\u003eGathering the Data 374\u003c\/p\u003e \u003cp\u003eA Simple Model 376\u003c\/p\u003e \u003cp\u003eModeling Individual Uplift 376\u003c\/p\u003e \u003cp\u003eComputing Uplift with R 378\u003c\/p\u003e \u003cp\u003eUsing the Results of an Uplift Model 378\u003c\/p\u003e \u003cp\u003e14.3 Reinforcement Learning 380\u003c\/p\u003e \u003cp\u003eExplore-Exploit: Multi-armed Bandits 380\u003c\/p\u003e \u003cp\u003eExample of Using a Contextual Multi-Arm Bandit for Movie Recommendations 382\u003c\/p\u003e \u003cp\u003eMarkov Decision Process (MDP) 383\u003c\/p\u003e \u003cp\u003e14.4 Summary 388\u003c\/p\u003e \u003cp\u003eProblems 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Mining Relationships Among Records\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Association Rules and Collaborative Filtering 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Association Rules 394\u003c\/p\u003e \u003cp\u003eDiscovering Association Rules in Transaction Databases 394\u003c\/p\u003e \u003cp\u003eExample 1: Synthetic Data on Purchases of Phone Faceplates 394\u003c\/p\u003e \u003cp\u003eGenerating Candidate Rules 395\u003c\/p\u003e \u003cp\u003eThe Apriori Algorithm 397\u003c\/p\u003e \u003cp\u003eSelecting Strong Rules 397\u003c\/p\u003e \u003cp\u003eData Format 399\u003c\/p\u003e \u003cp\u003eThe Process of Rule Selection 400\u003c\/p\u003e \u003cp\u003eInterpreting the Results 401\u003c\/p\u003e \u003cp\u003eRules and Chance 403\u003c\/p\u003e \u003cp\u003eExample 2: Rules for Similar Book Purchases 405\u003c\/p\u003e \u003cp\u003e15.2 Collaborative Filtering 407\u003c\/p\u003e \u003cp\u003eData Type and Format 407\u003c\/p\u003e \u003cp\u003eExample 3: Netflix Prize Contest 408\u003c\/p\u003e \u003cp\u003eUser-Based Collaborative Filtering: “People Like You” 409\u003c\/p\u003e \u003cp\u003eItem-Based Collaborative Filtering 411\u003c\/p\u003e \u003cp\u003eEvaluating Performance 412\u003c\/p\u003e \u003cp\u003eExample 4: Predicting Movie Ratings with MovieLens Data 413\u003c\/p\u003e \u003cp\u003eAdvantages and Weaknesses of Collaborative Filtering 416\u003c\/p\u003e \u003cp\u003eCollaborative Filtering vs. Association Rules 417\u003c\/p\u003e \u003cp\u003e15.3 Summary 419\u003c\/p\u003e \u003cp\u003eProblems 421\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Cluster Analysis 425\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 426\u003c\/p\u003e \u003cp\u003eExample: Public Utilities 427\u003c\/p\u003e \u003cp\u003e16.2 Measuring Distance Between Two Records 429\u003c\/p\u003e \u003cp\u003eEuclidean Distance 429\u003c\/p\u003e \u003cp\u003eNormalizing Numerical Variables 430\u003c\/p\u003e \u003cp\u003eOther Distance Measures for Numerical Data 432\u003c\/p\u003e \u003cp\u003eDistance Measures for Categorical Data 433\u003c\/p\u003e \u003cp\u003eDistance Measures for Mixed Data 434\u003c\/p\u003e \u003cp\u003e16.3 Measuring Distance Between Two Clusters 434\u003c\/p\u003e \u003cp\u003eMinimum Distance 434\u003c\/p\u003e \u003cp\u003eMaximum Distance 435\u003c\/p\u003e \u003cp\u003eAverage Distance 435\u003c\/p\u003e \u003cp\u003eCentroid Distance 435\u003c\/p\u003e \u003cp\u003e16.4 Hierarchical (Agglomerative) Clustering 437\u003c\/p\u003e \u003cp\u003eSingle Linkage 437\u003c\/p\u003e \u003cp\u003eComplete Linkage 438\u003c\/p\u003e \u003cp\u003eAverage Linkage 438\u003c\/p\u003e \u003cp\u003eCentroid Linkage 438\u003c\/p\u003e \u003cp\u003eWard’s Method 438\u003c\/p\u003e \u003cp\u003eDendrograms: Displaying Clustering Process and Results 439\u003c\/p\u003e \u003cp\u003eValidating Clusters 441\u003c\/p\u003e \u003cp\u003eLimitations of Hierarchical Clustering 443\u003c\/p\u003e \u003cp\u003e16.5 Non-Hierarchical Clustering: The k-Means Algorithm 444\u003c\/p\u003e \u003cp\u003eChoosing the Number of Clusters (k) 445\u003c\/p\u003e \u003cp\u003eProblems 450\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII Forecasting Time Series\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Handling Time Series 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 455\u003c\/p\u003e \u003cp\u003e17.2 Descriptive vs. Predictive Modeling 457\u003c\/p\u003e \u003cp\u003e17.3 Popular Forecasting Methods in Business 457\u003c\/p\u003e \u003cp\u003eProblems 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Regression-Based Forecasting 469\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 A Model with Trend 469\u003c\/p\u003e \u003cp\u003eLinear Trend 469\u003c\/p\u003e \u003cp\u003eExponential Trend 473\u003c\/p\u003e \u003cp\u003ePolynomial Trend 474\u003c\/p\u003e \u003cp\u003eProblems 489\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Smoothing and Deep Learning Methods for Forecasting 499\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Smoothing Methods: Introduction 500\u003c\/p\u003e \u003cp\u003e19.2 Moving Average 500\u003c\/p\u003e \u003cp\u003eCentered Moving Average for Visualization 500\u003c\/p\u003e \u003cp\u003eTrailing Moving Average for Forecasting 501\u003c\/p\u003e \u003cp\u003eChoosing Window Width (w) 504\u003c\/p\u003e \u003cp\u003eProblems 516\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Social Network Analytics 527\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 527\u003c\/p\u003e \u003cp\u003e20.2 Directed vs. Undirected Networks 529\u003c\/p\u003e \u003cp\u003e20.3 Visualizing and Analyzing Networks 530\u003c\/p\u003e \u003cp\u003ePlot Layout 530\u003c\/p\u003e \u003cp\u003eEdge List 533\u003c\/p\u003e \u003cp\u003eAdjacency Matrix 533\u003c\/p\u003e \u003cp\u003eUsing Network Data in Classification and Prediction 534\u003c\/p\u003e \u003cp\u003eProblems 548\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Text Mining 549\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 549\u003c\/p\u003e \u003cp\u003e21.2 The Tabular Representation of Text 550\u003c\/p\u003e \u003cp\u003e21.3 Bag-of-Words vs. Meaning Extraction at Document Level 551\u003c\/p\u003e \u003cp\u003eProblems 570\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Responsible Data Science 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 573\u003c\/p\u003e \u003cp\u003e22.2 Unintentional Harm 574\u003c\/p\u003e \u003cp\u003e22.3 Legal Considerations 576\u003c\/p\u003e \u003cp\u003e22.4 Principles of Responsible Data Science 577\u003c\/p\u003e \u003cp\u003eNon-maleficence 578\u003c\/p\u003e \u003cp\u003eFairness 578\u003c\/p\u003e \u003cp\u003eTransparency 579\u003c\/p\u003e \u003cp\u003eAccountability 580\u003c\/p\u003e \u003cp\u003eData Privacy and Security 580\u003c\/p\u003e \u003cp\u003eProblems 599\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IX Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23 Cases 603\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Charles Book Club 603\u003c\/p\u003e \u003cp\u003eThe Book Industry 603\u003c\/p\u003e \u003cp\u003eDatabase Marketing at Charles 604\u003c\/p\u003e \u003cp\u003eMachine Learning Techniques 606\u003c\/p\u003e \u003cp\u003eAssignment 608\u003c\/p\u003e \u003cp\u003e23.2 German Credit 610\u003c\/p\u003e \u003cp\u003eBackground 610\u003c\/p\u003e \u003cp\u003eData 610\u003c\/p\u003e \u003cp\u003eAssignment 614\u003c\/p\u003e \u003cp\u003eIndex 647\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGalit Shmueli, PhD,\u003c\/b\u003e is Distinguished Professor and Institute Director at National Tsing Hua University’s Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePeter C. Bruce,\u003c\/b\u003e is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePeter Gedeck, PhD,\u003c\/b\u003e is Senior Data Scientist at Collaborative Drug Discovery and teaches at statistics.com and the UVA School of Data Science. His specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eInbal Yahav, PhD,\u003c\/b\u003e is a Senior Lecturer in The Coller School of Management at Tel Aviv University, Israel. Her work focuses on the development and adaptation of statistical models for use by researchers in the field of information systems. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNitin R. Patel, PhD,\u003c\/b\u003e is Co-founder and Lead Researcher at Cytel Inc. He was also a Co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University, USA.    \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMachine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications in R\u003c\/i\u003e provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. \u003c\/p\u003e\u003cp\u003eThis is the second R edition of \u003ci\u003eMachine Learning for Business Analytics\u003c\/i\u003e. This edition also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R \u003c\/li\u003e \u003cli\u003e An expanded chapter focused on discussion of deep learning techniques\u003c\/li\u003e \u003cli\u003e A new chapter on experimental feedback techniques including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e \u003cli\u003e A new chapter on responsible data science\u003c\/li\u003e \u003cli\u003e Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003e A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques   \u003c\/li\u003e \u003cli\u003e End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003e A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548777701,"sku":"NP9781119835172","price":111.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119835172.jpg?v=1761784552","url":"https:\/\/k12savings.com\/products\/machine-learning-for-business-analytics-isbn-9781119835172","provider":"K12savings","version":"1.0","type":"link"}