Machine learning for business analytics : concepts, techniques and applications in R

著者

書誌事項

Machine learning for business analytics : concepts, techniques and applications in R

Galit Shmueli ... [et al.]

J. Wiley, 2023

2nd ed

  • : hardback

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注記

First edition published as Data mining for business analytics: concepts, techniques, and applications in R (2017)

Includes bibliographical references (p. 639-640) and index

内容説明・目次

内容説明

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine 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. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R 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. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science 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 A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This 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.

目次

Foreword by Ravi Bapna xix Foreword by Gareth James xxi Preface to the Second R Edition xxiii Acknowledgments xxvi Part I Preliminaries Chapter 1 Introduction 3 1.1 What Is Business Analytics? 3 1.2 What Is Machine Learning? 5 1.3 Machine Learning, AI, and Related Terms 5 1.4 Big Data 7 1.5 Data Science 8 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 13 Chapter 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 2.3 The Steps in a Machine Learning Project 21 2.4 Preliminary Steps 23 2.5 Predictive Power and Overfitting 35 2.6 Building a Predictive Model 41 2.7 Using R for Machine Learning on a Local Machine 46 2.8 Automating Machine Learning Solutions 47 2.9 Ethical Practice in Machine Learning 52 Problems 57 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 63 3.1 Uses of Data Visualization 63 3.2 Data Examples 65 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 67 3.4 Multidimensional Visualization 75 3.5 Specialized Visualizations 91 3.6 Major Visualizations and Operations, by Machine Learning Goal 97 Problems 99 Chapter 4 Dimension Reduction 101 4.1 Introduction 101 4.2 Curse of Dimensionality 102 4.3 Practical Considerations 102 4.4 Data Summaries 103 4.5 Correlation Analysis 107 4.6 Reducing the Number of Categories in Categorical Variables 109 4.7 Converting a Categorical Variable to a Numerical Variable 111 4.8 Principal Component Analysis 111 4.9 Dimension Reduction Using Regression Models 121 4.10 Dimension Reduction Using Classification and Regression Trees 121 Problems 123 Part III Performance Evaluation Chapter 5 Evaluating Predictive Performance 129 5.1 Introduction 130 5.2 Evaluating Predictive Performance 130 5.3 Judging Classifier Performance 136 5.4 Judging Ranking Performance 150 5.5 Oversampling 156 Problems 162 Part IV Prediction and Classification Methods Chapter 6 Multiple Linear Regression 167 6.1 Introduction 167 6.2 Explanatory vs. Predictive Modeling 168 6.3 Estimating the Regression Equation and Prediction 170 6.4 Variable Selection in Linear Regression 176 Problems 188 Chapter 7 k-Nearest Neighbors (kNN) 193 7.1 The k-NN Classifier (Categorical Outcome) 193 7.2 k-NN for a Numerical Outcome 201 7.3 Advantages and Shortcomings of k-NN Algorithms 204 Problems 205 Chapter 8 The Naive Bayes Classifier 207 8.1 Introduction 207 8.2 Applying the Full (Exact) Bayesian Classifier 209 8.3 Solution: Naive Bayes 211 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 220 Problems 223 Chapter 9 Classification and Regression Trees 225 9.1 Introduction 226 9.2 Classification Trees 228 9.3 Evaluating the Performance of a Classification Tree 235 9.4 Avoiding Overfitting 239 9.5 Classification Rules from Trees 247 9.6 Classification Trees for More Than Two Classes 248 9.7 Regression Trees 249 9.8 Advantages and Weaknesses of a Tree 250 9.9 Improving Prediction: Random Forests and Boosted Trees 252 Problems 257 Chapter 10 Logistic Regression 261 10.1 Introduction 261 10.2 The Logistic Regression Model 263 10.3 Example: Acceptance of Personal Loan 264 10.4 Evaluating Classification Performance 271 10.5 Variable Selection 273 10.6 Logistic Regression for Multi-Class Classification 274 10.7 Example of Complete Analysis: Predicting Delayed Flights 277 Problems 289 Chapter 11 Neural Nets 293 11.1 Introduction 293 11.2 Concept and Structure of a Neural Network 294 11.3 Fitting a Network to Data 295 11.4 Required User Input 307 11.5 Exploring the Relationship Between Predictors and Outcome 308 11.6 Deep Learning 309 11.7 Advantages and Weaknesses of Neural Networks 320 Problems 322 Chapter 12 Discriminant Analysis 325 12.1 Introduction 325 12.2 Distance of a Record from a Class 327 12.3 Fisher's Linear Classification Functions 329 12.4 Classification Performance of Discriminant Analysis 333 12.5 Prior Probabilities 334 12.6 Unequal Misclassification Costs 334 12.7 Classifying More Than Two Classes 336 12.8 Advantages and Weaknesses 339 Problems 341 Chapter 13 Generating, Comparing, and Combining Multiple Models 345 13.1 Ensembles 346 13.2 Automated Machine Learning (AutoML) 352 13.3 Explaining Model Predictions 358 13.4 Summary 360 Problems 362 Part V Intervention and User Feedback Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 367 14.1 A/B Testing 368 14.2 Uplift (Persuasion) Modeling 373 14.3 Reinforcement Learning 380 14.4 Summary 388 Problems 390 Part VI Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 393 15.1 Association Rules 394 15.2 Collaborative Filtering 407 15.3 Summary 419 Problems 421 Chapter 16 Cluster Analysis 425 16.1 Introduction 426 16.2 Measuring Distance Between Two Records 429 16.3 Measuring Distance Between Two Clusters 434 16.4 Hierarchical (Agglomerative) Clustering 437 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 444 Problems 450 Part VII Forecasting Time Series Chapter 17 Handling Time Series 455 17.1 Introduction 455 17.2 Descriptive vs. Predictive Modeling 457 17.3 Popular Forecasting Methods in Business 457 17.4 Time Series Components 458 17.5 Data Partitioning and Performance Evaluation 463 Problems 466 Chapter 18 Regression-Based Forecasting 469 18.1 A Model with Trend 469 18.2 A Model with Seasonality 476 18.3 A Model with Trend and Seasonality 478 18.4 Autocorrelation and ARIMA Models 479 Problems 489 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 499 19.1 Smoothing Methods: Introduction 500 19.2 Moving Average 500 19.3 Simple Exponential Smoothing 505 19.4 Advanced Exponential Smoothing 507 19.5 Deep Learning for Forecasting 511 Problems 516 Part VIII Data Analytics Chapter 20 Social Network Analytics 527 20.1 Introduction 527 20.2 Directed vs. Undirected Networks 529 20.3 Visualizing and Analyzing Networks 530 20.4 Social Data Metrics and Taxonomy 534 20.5 Using Network Metrics in Prediction and Classification 538 20.6 Collecting Social Network Data with R 545 20.7 Advantages and Disadvantages 545 Problems 548 Chapter 21 Text Mining 549 21.1 Introduction 549 21.2 The Tabular Representation of Text 550 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 551 21.4 Preprocessing the Text 552 21.5 Implementing Machine Learning Methods 560 21.6 Example: Online Discussions on Autos and Electronics 560 21.7 Example: Sentiment Analysis of Movie Reviews 564 21.8 Summary 568 Problems 570 Chapter 22 Responsible Data Science 573 22.1 Introduction 573 22.2 Unintentional Harm 574 22.3 Legal Considerations 576 22.4 Principles of Responsible Data Science 577 22.5 A Responsible Data Science Framework 580 22.6 Documentation Tools 584 22.7 Example: Applying the RDS Framework to the COMPAS Example 588 22.8 Summary 598 Problems 599 Part IX Cases Chapter 23 Cases 603 23.1 Charles Book Club 603 23.2 German Credit 610 23.3 Tayko Software Cataloger 615 23.4 Political Persuasion 619 23.5 Taxi Cancellations 623 23.6 Segmenting Consumers of Bath Soap 625 23.7 Direct-Mail Fundraising 629 23.8 Catalog Cross-Selling 632 23.9 Time Series Case: Forecasting Public Transportation Demand 634 23.10 Loan Approval 636 Index 647

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