Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning…mehr
Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. 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 Python edition of Machine Learning for Business Analytics. This edition also includes: * A new chapter on generative AI (large language models or LLMs, and image generation) * An expanded chapter on deep learning * 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 of cases demonstrating applications for the machine learning techniques * End-of-chapter exercises with data * 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 AI, 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.
Galit Shmueli, PhD, is Chair Professor at National Tsing Hua University's Institute of Service Science, College of Technology Management. 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. Peter C. Bruce is the Founder and former President of the Institute for Statistics Education at Statistics.com. Peter Gedeck, PhD, is Senior Data Scientist at Collaborative Drug Discovery and Lecturer at the UVA School of Data Science. His speciality is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Nitin R. Patel, PhD, is cofounder 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. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.
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Foreword by Gareth James xxi Preface to the Second Python Edition xxiii Acknowledgments xxvii 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 12 Order of Topics 13 Chapter 2 Overview of the Machine Learning Process 17 2.1 Introduction 18 2.2 Core Ideas in Machine Learning 18 2.3 The Steps in a Machine Learning Project 22 2.4 Preliminary Steps 23 2.5 Predictive Power and Overfitting 37 2.6 Building a Predictive Model 43 2.7 Using Python for Machine Learning on a Local Machine 49 2.8 Automating Machine Learning Solutions 49 2.9 Ethical Practice in Machine Learning 54 Problems 55 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 61 3.1 Uses of Data Visualization 62 3.2 Data Examples 64 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66 3.4 Multidimensional Visualization 75 3.5 Specialized Visualizations 90 Problems 98 Chapter 4 Dimension Reduction 101 4.1 Introduction 102 4.2 Curse of Dimensionality 102 4.3 Practical Considerations 103 4.4 Data Summaries 103 4.5 Correlation Analysis 108 4.6 Reducing the Number of Categories in Categorical Variables 109 4.7 Converting a Categorical Variable to a Numerical Variable 109 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 131 5.3 Judging Classifier Performance 137 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 168 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 (k-NN) 193 7.1 The k-NN Classifier (Categorical Outcome) 194 7.2 k-NN for a Numerical Outcome 203 7.3 Advantages and Shortcomings of k-NN Algorithms 205 Problems 207 Chapter 8 The Naive Bayes Classifier 209 8.1 Introduction 209 8.2 Applying the Full (Exact) Bayesian Classifier 212 8.3 Solution: Naive Bayes 213 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 224 Problems 226 Chapter 9 Classification and Regression Trees 229 9.1 Introduction 230 9.2 Classification Trees 232 9.3 Evaluating the Performance of a Classification Tree 241 9.4 Avoiding Overfitting 246 9.5 Classification Rules from Trees 252 9.6 Classification Trees for More Than Two Classes 252 9.7 Regression Trees 253 9.8 Advantages and Weaknesses of a Tree 256 9.9 Improving Prediction: Random Forests and Boosted Trees 258 Problems 264 Chapter 10 Logistic Regression 267 10.1 Introduction 268 10.2 The Logistic Regression Model 269 10.3 Example: Acceptance of Personal Loan 272 10.4 Evaluating Classification Performance 277 10.5 Variable Selection 280 10.6 Logistic Regression for Multi-Class Classification 281 10.7 Example of Complete Analysis: Predicting Delayed Flights 285 Problems 298 Chapter 11 Neural Nets 301 11.1 Introduction 302 11.2 Concept and Structure of a Neural Network 302 11.3 Fitting a Network to Data 303 11.4 Required User Input 316 11.5 Exploring the Relationship Between Predictors and Outcome 317 11.6 Deep Learning 318 11.7 Advantages and Weaknesses of Neural Networks 329 Problems 331 Chapter 12 Discriminant Analysis 333 12.1 Introduction 334 12.2 Distance of a Record from a Class 336 12.3 Fisher's Linear Classification Functions 337 12.4 Classification Performance of Discriminant Analysis 341 12.5 Prior Probabilities 342 12.6 Unequal Misclassification Costs 342 12.7 Classifying More Than Two Classes 344 12.8 Advantages and Weaknesses 347 Problems 348 Chapter 13 Generating, Comparing, and Combining Multiple Models 351 13.1 Ensembles 352 13.2 Automated Machine Learning (AutoML) 359 13.3 Explaining Model Predictions 365 13.4 Summary 366 Problems 368 Chapter 14 Experiments, Uplift Models, and Reinforcement Learning 371 14.1 A/B Testing 372 14.2 Uplift (Persuasion) Modeling 377 14.3 Reinforcement Learning 384 14.4 Summary 393 Problems 395 Part V Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 399 15.1 Association Rules 400 15.2 Collaborative Filtering 413 15.3 Summary 427 Problems 429 Chapter 16 Cluster Analysis 433 16.1 Introduction 434 16.2 Measuring Distance Between Two Records 437 16.3 Measuring Distance Between Two Clusters 443 16.4 Hierarchical (Agglomerative) Clustering 445 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 453 Problems 459 Part VI Forecasting Time Series Chapter 17 Handling Time Series 463 17.1 Introduction 464 17.2 Descriptive vs. Predictive Modeling 465 17.3 Popular Forecasting Methods in Business 465 17.4 Time Series Components 466 17.5 Data Partitioning and Performance Evaluation 470 Problems 474 Chapter 18 Regression-Based Forecasting 477 18.1 A Model with Trend 478 18.2 A Model with Seasonality 484 18.3 A Model with Trend and Seasonality 486 18.4 Autocorrelation and ARIMA Models 488 Problems 498 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 509 19.1 Smoothing Methods: Introduction 510 19.2 Moving Average 510 19.3 Simple Exponential Smoothing 515 19.4 Advanced Exponential Smoothing 518 19.5 Deep Learning for Forecasting 521 Problems 527 Part VII Data Analytics Chapter 20 Social Network Analytics 537 20.1 Introduction 538 20.2 Directed vs. Undirected Networks 538 20.3 Visualizing and Analyzing Networks 539 20.4 Social Data Metrics and Taxonomy 544 20.5 Using Network Metrics in Prediction and Classification 550 20.6 Business Uses of Social Network Analysis 556 20.7 Summary 557 Problems 559 Chapter 21 Text Mining 561 21.1 Introduction 562 21.2 The Tabular Representation of Text 562 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 563 21.4 Preprocessing the Text 564 21.5 Implementing Machine Learning Methods 573 21.6 Example: Online Discussions on Autos and Electronics 573 21.7 Deep Learning Approaches 577 21.8 Example: Sentiment Analysis of Movie Reviews 578 21.9 Summary 581 Problems 584 Chapter 22 Responsible Data Science 587 22.1 Introduction 588 22.2 Unintentional Harm 589 22.3 Legal Considerations 591 22.4 Principles of Responsible Data Science 592 22.5 A Responsible Data Science Framework 595 22.6 Documentation Tools 599 22.7 Example: Applying the RDS Framework to the COMPAS Example 603 22.8 Summary 613 Problems 614 Chapter 23 Generative AI 617 23.1 The Transformative Power of Generative AI 617 23.2 What is Generative AI? 619 23.3 Data and Infrastructure Requirements 621 23.4 Adapting Models for Specific Purposes 623 23.5 Prompt Engineering 624 23.6 Uses of Generative AI 625 23.7 Caveats and Concerns 629 23.8 Summary 631 Problems 633 Part VIII Cases Chapter 24 Cases 639 24.1 Charles Book Club 639 24.2 German Credit 646 24.3 Tayko Software Cataloger 651 24.4 Political Persuasion 655 24.5 Taxi Cancellations 659 24.7 Direct-Mail Fundraising 665 24.8 Catalog Cross-Selling 668 24.9 Time-Series Case: Forecasting Public Transportation Demand 670 24.10 Loan Approval 672 References 675 Index 677
Foreword by Gareth James xxi Preface to the Second Python Edition xxiii Acknowledgments xxvii 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 12 Order of Topics 13 Chapter 2 Overview of the Machine Learning Process 17 2.1 Introduction 18 2.2 Core Ideas in Machine Learning 18 2.3 The Steps in a Machine Learning Project 22 2.4 Preliminary Steps 23 2.5 Predictive Power and Overfitting 37 2.6 Building a Predictive Model 43 2.7 Using Python for Machine Learning on a Local Machine 49 2.8 Automating Machine Learning Solutions 49 2.9 Ethical Practice in Machine Learning 54 Problems 55 Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 61 3.1 Uses of Data Visualization 62 3.2 Data Examples 64 3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66 3.4 Multidimensional Visualization 75 3.5 Specialized Visualizations 90 Problems 98 Chapter 4 Dimension Reduction 101 4.1 Introduction 102 4.2 Curse of Dimensionality 102 4.3 Practical Considerations 103 4.4 Data Summaries 103 4.5 Correlation Analysis 108 4.6 Reducing the Number of Categories in Categorical Variables 109 4.7 Converting a Categorical Variable to a Numerical Variable 109 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 131 5.3 Judging Classifier Performance 137 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 168 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 (k-NN) 193 7.1 The k-NN Classifier (Categorical Outcome) 194 7.2 k-NN for a Numerical Outcome 203 7.3 Advantages and Shortcomings of k-NN Algorithms 205 Problems 207 Chapter 8 The Naive Bayes Classifier 209 8.1 Introduction 209 8.2 Applying the Full (Exact) Bayesian Classifier 212 8.3 Solution: Naive Bayes 213 8.4 Advantages and Shortcomings of the Naive Bayes Classifier 224 Problems 226 Chapter 9 Classification and Regression Trees 229 9.1 Introduction 230 9.2 Classification Trees 232 9.3 Evaluating the Performance of a Classification Tree 241 9.4 Avoiding Overfitting 246 9.5 Classification Rules from Trees 252 9.6 Classification Trees for More Than Two Classes 252 9.7 Regression Trees 253 9.8 Advantages and Weaknesses of a Tree 256 9.9 Improving Prediction: Random Forests and Boosted Trees 258 Problems 264 Chapter 10 Logistic Regression 267 10.1 Introduction 268 10.2 The Logistic Regression Model 269 10.3 Example: Acceptance of Personal Loan 272 10.4 Evaluating Classification Performance 277 10.5 Variable Selection 280 10.6 Logistic Regression for Multi-Class Classification 281 10.7 Example of Complete Analysis: Predicting Delayed Flights 285 Problems 298 Chapter 11 Neural Nets 301 11.1 Introduction 302 11.2 Concept and Structure of a Neural Network 302 11.3 Fitting a Network to Data 303 11.4 Required User Input 316 11.5 Exploring the Relationship Between Predictors and Outcome 317 11.6 Deep Learning 318 11.7 Advantages and Weaknesses of Neural Networks 329 Problems 331 Chapter 12 Discriminant Analysis 333 12.1 Introduction 334 12.2 Distance of a Record from a Class 336 12.3 Fisher's Linear Classification Functions 337 12.4 Classification Performance of Discriminant Analysis 341 12.5 Prior Probabilities 342 12.6 Unequal Misclassification Costs 342 12.7 Classifying More Than Two Classes 344 12.8 Advantages and Weaknesses 347 Problems 348 Chapter 13 Generating, Comparing, and Combining Multiple Models 351 13.1 Ensembles 352 13.2 Automated Machine Learning (AutoML) 359 13.3 Explaining Model Predictions 365 13.4 Summary 366 Problems 368 Chapter 14 Experiments, Uplift Models, and Reinforcement Learning 371 14.1 A/B Testing 372 14.2 Uplift (Persuasion) Modeling 377 14.3 Reinforcement Learning 384 14.4 Summary 393 Problems 395 Part V Mining Relationships Among Records Chapter 15 Association Rules and Collaborative Filtering 399 15.1 Association Rules 400 15.2 Collaborative Filtering 413 15.3 Summary 427 Problems 429 Chapter 16 Cluster Analysis 433 16.1 Introduction 434 16.2 Measuring Distance Between Two Records 437 16.3 Measuring Distance Between Two Clusters 443 16.4 Hierarchical (Agglomerative) Clustering 445 16.5 Non-Hierarchical Clustering: The k-Means Algorithm 453 Problems 459 Part VI Forecasting Time Series Chapter 17 Handling Time Series 463 17.1 Introduction 464 17.2 Descriptive vs. Predictive Modeling 465 17.3 Popular Forecasting Methods in Business 465 17.4 Time Series Components 466 17.5 Data Partitioning and Performance Evaluation 470 Problems 474 Chapter 18 Regression-Based Forecasting 477 18.1 A Model with Trend 478 18.2 A Model with Seasonality 484 18.3 A Model with Trend and Seasonality 486 18.4 Autocorrelation and ARIMA Models 488 Problems 498 Chapter 19 Smoothing and Deep Learning Methods for Forecasting 509 19.1 Smoothing Methods: Introduction 510 19.2 Moving Average 510 19.3 Simple Exponential Smoothing 515 19.4 Advanced Exponential Smoothing 518 19.5 Deep Learning for Forecasting 521 Problems 527 Part VII Data Analytics Chapter 20 Social Network Analytics 537 20.1 Introduction 538 20.2 Directed vs. Undirected Networks 538 20.3 Visualizing and Analyzing Networks 539 20.4 Social Data Metrics and Taxonomy 544 20.5 Using Network Metrics in Prediction and Classification 550 20.6 Business Uses of Social Network Analysis 556 20.7 Summary 557 Problems 559 Chapter 21 Text Mining 561 21.1 Introduction 562 21.2 The Tabular Representation of Text 562 21.3 Bag-of-Words vs. Meaning Extraction at Document Level 563 21.4 Preprocessing the Text 564 21.5 Implementing Machine Learning Methods 573 21.6 Example: Online Discussions on Autos and Electronics 573 21.7 Deep Learning Approaches 577 21.8 Example: Sentiment Analysis of Movie Reviews 578 21.9 Summary 581 Problems 584 Chapter 22 Responsible Data Science 587 22.1 Introduction 588 22.2 Unintentional Harm 589 22.3 Legal Considerations 591 22.4 Principles of Responsible Data Science 592 22.5 A Responsible Data Science Framework 595 22.6 Documentation Tools 599 22.7 Example: Applying the RDS Framework to the COMPAS Example 603 22.8 Summary 613 Problems 614 Chapter 23 Generative AI 617 23.1 The Transformative Power of Generative AI 617 23.2 What is Generative AI? 619 23.3 Data and Infrastructure Requirements 621 23.4 Adapting Models for Specific Purposes 623 23.5 Prompt Engineering 624 23.6 Uses of Generative AI 625 23.7 Caveats and Concerns 629 23.8 Summary 631 Problems 633 Part VIII Cases Chapter 24 Cases 639 24.1 Charles Book Club 639 24.2 German Credit 646 24.3 Tayko Software Cataloger 651 24.4 Political Persuasion 655 24.5 Taxi Cancellations 659 24.7 Direct-Mail Fundraising 665 24.8 Catalog Cross-Selling 668 24.9 Time-Series Case: Forecasting Public Transportation Demand 670 24.10 Loan Approval 672 References 675 Index 677
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