{"product_id":"machine-learning-for-business-analytics-isbn-9781394286799","title":"Machine Learning for Business Analytics","description":"\u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThis is the second Python edition of Machine Learning for Business Analytics. This edition also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA new chapter on generative AI (large language models or LLMs, and image generation)\u003c\/li\u003e\n\u003cli\u003eAn expanded chapter on deep learning\u003c\/li\u003e\n\u003cli\u003eA new chapter on experimental feedback techniques including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e\n\u003cli\u003eA new chapter on responsible data science\u003c\/li\u003e\n\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\n\u003cli\u003eA full chapter of cases demonstrating applications for the machine learning techniques\u003c\/li\u003e\n\u003cli\u003eEnd-of-chapter exercises with data\u003c\/li\u003e\n\u003cli\u003eA 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 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. \u003c\/p\u003e\u003cp\u003eForeword by Gareth James xxi\u003c\/p\u003e \u003cp\u003ePreface to the Second Python Edition xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\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 12\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 18\u003c\/p\u003e \u003cp\u003e2.2 Core Ideas in Machine Learning 18\u003c\/p\u003e \u003cp\u003e2.3 The Steps in a Machine Learning Project 22\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 23\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 37\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model 43\u003c\/p\u003e \u003cp\u003e2.7 Using Python for Machine Learning on a Local Machine 49\u003c\/p\u003e \u003cp\u003e2.8 Automating Machine Learning Solutions 49\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Machine Learning 54\u003c\/p\u003e \u003cp\u003eProblems 55\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 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Uses of Data Visualization 62\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 64\u003c\/p\u003e \u003cp\u003e3.3 Basic Charts: Bar Charts, Line Charts, and Scatter Plots 66\u003c\/p\u003e \u003cp\u003e3.4 Multidimensional Visualization 75\u003c\/p\u003e \u003cp\u003e3.5 Specialized Visualizations 90\u003c\/p\u003e \u003cp\u003eProblems 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Dimension Reduction 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 102\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 102\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 103\u003c\/p\u003e \u003cp\u003e4.4 Data Summaries 103\u003c\/p\u003e \u003cp\u003e4.5 Correlation Analysis 108\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 109\u003c\/p\u003e \u003cp\u003e4.8 Principal Component Analysis 111\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 131\u003c\/p\u003e \u003cp\u003e5.3 Judging Classifier Performance 137\u003c\/p\u003e \u003cp\u003e5.4 Judging Ranking Performance 150\u003c\/p\u003e \u003cp\u003e5.5 Oversampling 156\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 168\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\u003e6.4 Variable Selection in Linear Regression 176\u003c\/p\u003e \u003cp\u003eProblems 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 k-Nearest Neighbors (k-NN) 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The k-NN Classifier (Categorical Outcome) 194\u003c\/p\u003e \u003cp\u003e7.2 k-NN for a Numerical Outcome 203\u003c\/p\u003e \u003cp\u003e7.3 Advantages and Shortcomings of k-NN Algorithms 205\u003c\/p\u003e \u003cp\u003eProblems 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 The Naive Bayes Classifier 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 209\u003c\/p\u003e \u003cp\u003e8.2 Applying the Full (Exact) Bayesian Classifier 212\u003c\/p\u003e \u003cp\u003e8.3 Solution: Naive Bayes 213\u003c\/p\u003e \u003cp\u003e8.4 Advantages and Shortcomings of the Naive Bayes Classifier 224\u003c\/p\u003e \u003cp\u003eProblems 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Classification and Regression Trees 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 230\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 232\u003c\/p\u003e \u003cp\u003e9.3 Evaluating the Performance of a Classification Tree 241\u003c\/p\u003e \u003cp\u003e9.4 Avoiding Overfitting 246\u003c\/p\u003e \u003cp\u003e9.5 Classification Rules from Trees 252\u003c\/p\u003e \u003cp\u003e9.6 Classification Trees for More Than Two Classes 252\u003c\/p\u003e \u003cp\u003e9.7 Regression Trees 253\u003c\/p\u003e \u003cp\u003e9.8 Advantages and Weaknesses of a Tree 256\u003c\/p\u003e \u003cp\u003e9.9 Improving Prediction: Random Forests and Boosted Trees 258\u003c\/p\u003e \u003cp\u003eProblems 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Logistic Regression 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 268\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 269\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 272\u003c\/p\u003e \u003cp\u003e10.4 Evaluating Classification Performance 277\u003c\/p\u003e \u003cp\u003e10.5 Variable Selection 280\u003c\/p\u003e \u003cp\u003e10.6 Logistic Regression for Multi-Class Classification 281\u003c\/p\u003e \u003cp\u003e10.7 Example of Complete Analysis: Predicting Delayed Flights 285\u003c\/p\u003e \u003cp\u003eProblems 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Neural Nets 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 302\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 302\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 303\u003c\/p\u003e \u003cp\u003e11.4 Required User Input 316\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Outcome 317\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 318\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 329\u003c\/p\u003e \u003cp\u003eProblems 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Discriminant Analysis 333\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 334\u003c\/p\u003e \u003cp\u003e12.2 Distance of a Record from a Class 336\u003c\/p\u003e \u003cp\u003e12.3 Fisher’s Linear Classification Functions 337\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 341\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 342\u003c\/p\u003e \u003cp\u003e12.6 Unequal Misclassification Costs 342\u003c\/p\u003e \u003cp\u003e12.7 Classifying More Than Two Classes 344\u003c\/p\u003e \u003cp\u003e12.8 Advantages and Weaknesses 347\u003c\/p\u003e \u003cp\u003eProblems 348\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Generating, Comparing, and Combining Multiple Models 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Ensembles 352\u003c\/p\u003e \u003cp\u003e13.2 Automated Machine Learning (AutoML) 359\u003c\/p\u003e \u003cp\u003e13.3 Explaining Model Predictions 365\u003c\/p\u003e \u003cp\u003e13.4 Summary 366\u003c\/p\u003e \u003cp\u003eProblems 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Experiments, Uplift Models, and Reinforcement Learning 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A\/B Testing 372\u003c\/p\u003e \u003cp\u003e14.2 Uplift (Persuasion) Modeling 377\u003c\/p\u003e \u003cp\u003e14.3 Reinforcement Learning 384\u003c\/p\u003e \u003cp\u003e14.4 Summary 393\u003c\/p\u003e \u003cp\u003eProblems 395\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Mining Relationships Among Records\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Association Rules and Collaborative Filtering 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Association Rules 400\u003c\/p\u003e \u003cp\u003e15.2 Collaborative Filtering 413\u003c\/p\u003e \u003cp\u003e15.3 Summary 427\u003c\/p\u003e \u003cp\u003eProblems 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Cluster Analysis 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 434\u003c\/p\u003e \u003cp\u003e16.2 Measuring Distance Between Two Records 437\u003c\/p\u003e \u003cp\u003e16.3 Measuring Distance Between Two Clusters 443\u003c\/p\u003e \u003cp\u003e16.4 Hierarchical (Agglomerative) Clustering 445\u003c\/p\u003e \u003cp\u003e16.5 Non-Hierarchical Clustering: The k-Means Algorithm 453\u003c\/p\u003e \u003cp\u003eProblems 459\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Forecasting Time Series\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Handling Time Series 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 464\u003c\/p\u003e \u003cp\u003e17.2 Descriptive vs. Predictive Modeling 465\u003c\/p\u003e \u003cp\u003e17.3 Popular Forecasting Methods in Business 465\u003c\/p\u003e \u003cp\u003e17.4 Time Series Components 466\u003c\/p\u003e \u003cp\u003e17.5 Data Partitioning and Performance Evaluation 470\u003c\/p\u003e \u003cp\u003eProblems 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Regression-Based Forecasting 477\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 A Model with Trend 478\u003c\/p\u003e \u003cp\u003e18.2 A Model with Seasonality 484\u003c\/p\u003e \u003cp\u003e18.3 A Model with Trend and Seasonality 486\u003c\/p\u003e \u003cp\u003e18.4 Autocorrelation and ARIMA Models 488\u003c\/p\u003e \u003cp\u003eProblems 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Smoothing and Deep Learning Methods for Forecasting 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Smoothing Methods: Introduction 510\u003c\/p\u003e \u003cp\u003e19.2 Moving Average 510\u003c\/p\u003e \u003cp\u003e19.3 Simple Exponential Smoothing 515\u003c\/p\u003e \u003cp\u003e19.4 Advanced Exponential Smoothing 518\u003c\/p\u003e \u003cp\u003e19.5 Deep Learning for Forecasting 521\u003c\/p\u003e \u003cp\u003eProblems 527\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Social Network Analytics 537\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 538\u003c\/p\u003e \u003cp\u003e20.2 Directed vs. Undirected Networks 538\u003c\/p\u003e \u003cp\u003e20.3 Visualizing and Analyzing Networks 539\u003c\/p\u003e \u003cp\u003e20.4 Social Data Metrics and Taxonomy 544\u003c\/p\u003e \u003cp\u003e20.5 Using Network Metrics in Prediction and Classification 550\u003c\/p\u003e \u003cp\u003e20.6 Business Uses of Social Network Analysis 556\u003c\/p\u003e \u003cp\u003e20.7 Summary 557\u003c\/p\u003e \u003cp\u003eProblems 559\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Text Mining 561\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 562\u003c\/p\u003e \u003cp\u003e21.2 The Tabular Representation of Text 562\u003c\/p\u003e \u003cp\u003e21.3 Bag-of-Words vs. Meaning Extraction at Document Level 563\u003c\/p\u003e \u003cp\u003e21.4 Preprocessing the Text 564\u003c\/p\u003e \u003cp\u003e21.5 Implementing Machine Learning Methods 573\u003c\/p\u003e \u003cp\u003e21.6 Example: Online Discussions on Autos and Electronics 573\u003c\/p\u003e \u003cp\u003e21.7 Deep Learning Approaches 577\u003c\/p\u003e \u003cp\u003e21.8 Example: Sentiment Analysis of Movie Reviews 578\u003c\/p\u003e \u003cp\u003e21.9 Summary 581\u003c\/p\u003e \u003cp\u003eProblems 584\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Responsible Data Science 587\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 588\u003c\/p\u003e \u003cp\u003e22.2 Unintentional Harm 589\u003c\/p\u003e \u003cp\u003e22.3 Legal Considerations 591\u003c\/p\u003e \u003cp\u003e22.4 Principles of Responsible Data Science 592\u003c\/p\u003e \u003cp\u003e22.5 A Responsible Data Science Framework 595\u003c\/p\u003e \u003cp\u003e22.6 Documentation Tools 599\u003c\/p\u003e \u003cp\u003e22.7 Example: Applying the RDS Framework to the COMPAS Example 603\u003c\/p\u003e \u003cp\u003e22.8 Summary 613\u003c\/p\u003e \u003cp\u003eProblems 614\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23 Generative AI 617\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 The Transformative Power of Generative AI 617\u003c\/p\u003e \u003cp\u003e23.2 What is Generative AI? 619\u003c\/p\u003e \u003cp\u003e23.3 Data and Infrastructure Requirements 621\u003c\/p\u003e \u003cp\u003e23.4 Adapting Models for Specific Purposes 623\u003c\/p\u003e \u003cp\u003e23.5 Prompt Engineering 624\u003c\/p\u003e \u003cp\u003e23.6 Uses of Generative AI 625\u003c\/p\u003e \u003cp\u003e23.7 Caveats and Concerns 629\u003c\/p\u003e \u003cp\u003e23.8 Summary 631\u003c\/p\u003e \u003cp\u003eProblems 633\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24 Cases 639\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Charles Book Club 639\u003c\/p\u003e \u003cp\u003e24.2 German Credit 646\u003c\/p\u003e \u003cp\u003e24.3 Tayko Software Cataloger 651\u003c\/p\u003e \u003cp\u003e24.4 Political Persuasion 655\u003c\/p\u003e \u003cp\u003e24.5 Taxi Cancellations 659\u003c\/p\u003e \u003cp\u003e24.7 Direct-Mail Fundraising 665\u003c\/p\u003e \u003cp\u003e24.8 Catalog Cross-Selling 668\u003c\/p\u003e \u003cp\u003e24.9 Time-Series Case: Forecasting Public Transportation Demand 670\u003c\/p\u003e \u003cp\u003e24.10 Loan Approval 672\u003c\/p\u003e \u003cp\u003eReferences 675\u003c\/p\u003e \u003cp\u003eIndex 677\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGalit Shmueli, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePeter C. Bruce\u003c\/b\u003e is the Founder and former President of the Institute for Statistics Education at Statistics.com. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePeter Gedeck, PhD,\u003c\/b\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNitin R. Patel, PhD,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eThis is the second Python edition of Machine Learning for Business Analytics. This edition also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA new chapter on generative AI (large language models or LLMs, and image generation)\u003c\/li\u003e\n\u003cli\u003eAn expanded chapter on deep learning\u003c\/li\u003e\n\u003cli\u003eA new chapter on experimental feedback techniques including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e\n\u003cli\u003eA new chapter on responsible data science\u003c\/li\u003e\n\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\n\u003cli\u003eA full chapter of cases demonstrating applications for the machine learning techniques\u003c\/li\u003e\n\u003cli\u003eEnd-of-chapter exercises with data\u003c\/li\u003e\n\u003cli\u003eA 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 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548581093,"sku":"NP9781394286799","price":109.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394286799.jpg?v=1761784554","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-for-business-analytics-isbn-9781394286799","provider":"K12savings","version":"1.0","type":"link"}