{"product_id":"machine-learning-for-business-analytics-isbn-9781119828792","title":"Machine Learning for Business Analytics","description":"\u003cb\u003eMachine Learning for Business Analytics\u003c\/b\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 RapidMiner\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 seventh edition of \u003ci\u003eMachine Learning for Business Analytics\u003c\/i\u003e, and the first using RapidMiner software. This edition also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner\u003c\/li\u003e \u003cli\u003e Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years\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\u003cp\u003eForeword by \u003ci\u003eRavi Bapna\u003c\/i\u003e xxi\u003c\/p\u003e \u003cp\u003ePreface to the \u003ci\u003eRapidMiner\u003c\/i\u003e 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? 9\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\u003e1.9 Using RapidMiner Studio 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Overview of the Machine Learning Process 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 19\u003c\/p\u003e \u003cp\u003e2.2 Core Ideas in Machine Learning 20\u003c\/p\u003e \u003cp\u003e2.3 The Steps in a Machine Learning Project 23\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 25\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 32\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model with RapidMiner 37\u003c\/p\u003e \u003cp\u003e2.7 Using RapidMiner for Machine Learning 45\u003c\/p\u003e \u003cp\u003e2.8 Automating Machine Learning Solutions 47\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Machine Learning 52\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 Introduction 63\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 65\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 87\u003c\/p\u003e \u003cp\u003e3.6 Summary: Major Visualizations and Operations, by Machine Learning Goal 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Dimension Reduction 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 97\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 98\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 98\u003c\/p\u003e \u003cp\u003e4.4 Data Summaries 100\u003c\/p\u003e \u003cp\u003e4.5 Correlation Analysis 103\u003c\/p\u003e \u003cp\u003e4.6 Reducing the Number of Categories in Categorical Attributes 105\u003c\/p\u003e \u003cp\u003e4.7 Converting a Categorical Attribute to a Numerical Attribute 107\u003c\/p\u003e \u003cp\u003e4.8 Principal Component Analysis 107\u003c\/p\u003e \u003cp\u003e4.9 Dimension Reduction Using Regression Models 117\u003c\/p\u003e \u003cp\u003e4.10 Dimension Reduction Using Classification and Regression Trees 119\u003c\/p\u003e \u003cp\u003eProblems 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Performance Evaluation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Evaluating Predictive Performance 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 125\u003c\/p\u003e \u003cp\u003e5.2 Evaluating Predictive Performance 126\u003c\/p\u003e \u003cp\u003e5.3 Judging Classifier Performance 131\u003c\/p\u003e \u003cp\u003e5.4 Judging Ranking Performance 146\u003c\/p\u003e \u003cp\u003e5.5 Oversampling 151\u003c\/p\u003e \u003cp\u003eProblems 158\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 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 163\u003c\/p\u003e \u003cp\u003e6.2 Explanatory vs. Predictive Modeling 164\u003c\/p\u003e \u003cp\u003e6.3 Estimating the Regression Equation and Prediction 166\u003c\/p\u003e \u003cp\u003e6.4 Variable Selection in Linear Regression 171\u003c\/p\u003e \u003cp\u003eProblems 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 \u003ci\u003ek\u003c\/i\u003e-Nearest Neighbors (\u003ci\u003ek\u003c\/i\u003e-NN) 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The \u003ci\u003ek\u003c\/i\u003e-NN Classifier (Categorical Label) 189\u003c\/p\u003e \u003cp\u003e7.2 \u003ci\u003ek\u003c\/i\u003e-NN for a Numerical Label 200\u003c\/p\u003e \u003cp\u003e7.3 Advantages and Shortcomings of \u003ci\u003ek\u003c\/i\u003e-NN Algorithms 202\u003c\/p\u003e \u003cp\u003eAppendix: Computing Distances Between Records in RapidMiner 203\u003c\/p\u003e \u003cp\u003eProblems 205\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 211\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 229\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 232\u003c\/p\u003e \u003cp\u003e9.3 Evaluating the Performance of a Classification Tree 240\u003c\/p\u003e \u003cp\u003e9.4 Avoiding Overfitting 245\u003c\/p\u003e \u003cp\u003e9.5 Classification Rules from Trees 255\u003c\/p\u003e \u003cp\u003e9.6 Classification Trees for More Than Two Classes 256\u003c\/p\u003e \u003cp\u003e9.7 Regression Trees 256\u003c\/p\u003e \u003cp\u003e9.8 Improving Prediction: Random Forests and Boosted Trees 259\u003c\/p\u003e \u003cp\u003e9.9 Advantages and Weaknesses of a Tree 261\u003c\/p\u003e \u003cp\u003eProblems 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Logistic Regression 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 269\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 271\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 272\u003c\/p\u003e \u003cp\u003e10.4 Logistic Regression for Multi-class Classification 283\u003c\/p\u003e \u003cp\u003e10.5 Example of Complete Analysis: Predicting Delayed Flights 286\u003c\/p\u003e \u003cp\u003eAppendix: Logistic Regression for Ordinal Classes 299\u003c\/p\u003e \u003cp\u003eProblems 301\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Neural Networks 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 306\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 306\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 307\u003c\/p\u003e \u003cp\u003e11.4 Required User Input 321\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Target Attribute 322\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 323\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 334\u003c\/p\u003e \u003cp\u003eProblems 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Discriminant Analysis 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 337\u003c\/p\u003e \u003cp\u003e12.2 Distance of a Record from a Class 340\u003c\/p\u003e \u003cp\u003e12.3 Fisher’s Linear Classification Functions 341\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 346\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 348\u003c\/p\u003e \u003cp\u003e12.6 Unequal Misclassification Costs 348\u003c\/p\u003e \u003cp\u003e12.7 Classifying More Than Two Classes 349\u003c\/p\u003e \u003cp\u003e12.8 Advantages and Weaknesses 351\u003c\/p\u003e \u003cp\u003eProblems 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Generating, Comparing, and Combining Multiple Models 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Automated Machine Learning (AutoML) 359\u003c\/p\u003e \u003cp\u003e13.2 Explaining Model Predictions 367\u003c\/p\u003e \u003cp\u003e13.3 Ensembles 373\u003c\/p\u003e \u003cp\u003e13.4 Summary 381\u003c\/p\u003e \u003cp\u003eProblems 383\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 387\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 A\/B Testing 387\u003c\/p\u003e \u003cp\u003e14.2 Uplift (Persuasion) Modeling 393\u003c\/p\u003e \u003cp\u003e14.3 Reinforcement Learning 400\u003c\/p\u003e \u003cp\u003e14.4 Summary 405\u003c\/p\u003e \u003cp\u003eProblems 406\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 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Association Rules 409\u003c\/p\u003e \u003cp\u003e15.2 Collaborative Filtering 424\u003c\/p\u003e \u003cp\u003e15.3 Summary 438\u003c\/p\u003e \u003cp\u003eProblems 440\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Cluster Analysis 445\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 445\u003c\/p\u003e \u003cp\u003e16.2 Measuring Distance Between Two Records 449\u003c\/p\u003e \u003cp\u003e16.3 Measuring Distance Between Two Clusters 455\u003c\/p\u003e \u003cp\u003e16.4 Hierarchical (Agglomerative) Clustering 457\u003c\/p\u003e \u003cp\u003e16.5 Non-Hierarchical Clustering: The k-Means Algorithm 466\u003c\/p\u003e \u003cp\u003eProblems 473\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 479\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 480\u003c\/p\u003e \u003cp\u003e17.2 Descriptive vs. Predictive Modeling 481\u003c\/p\u003e \u003cp\u003e17.3 Popular Forecasting Methods in Business 481\u003c\/p\u003e \u003cp\u003e17.4 Time Series Components 482\u003c\/p\u003e \u003cp\u003e17.5 Data Partitioning and Performance Evaluation 486\u003c\/p\u003e \u003cp\u003eProblems 493\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Regression-Based Forecasting 497\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 A Model with Trend 498\u003c\/p\u003e \u003cp\u003e18.2 A Model with Seasonality 505\u003c\/p\u003e \u003cp\u003e18.3 A Model with Trend and Seasonality 508\u003c\/p\u003e \u003cp\u003e18.4 Autocorrelation and ARIMA Models 509\u003c\/p\u003e \u003cp\u003eProblems 521\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Smoothing and Deep Learning Methods for Forecasting 533\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Smoothing Methods: Introduction 534\u003c\/p\u003e \u003cp\u003e19.2 Moving Average 534\u003c\/p\u003e \u003cp\u003e19.3 Simple Exponential Smoothing 540\u003c\/p\u003e \u003cp\u003e19.4 Advanced Exponential Smoothing 545\u003c\/p\u003e \u003cp\u003e19.5 Deep Learning for Forecasting 549\u003c\/p\u003e \u003cp\u003eProblems 553\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Social Network Analytics 563\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 563\u003c\/p\u003e \u003cp\u003e20.2 Directed vs. Undirected Networks 564\u003c\/p\u003e \u003cp\u003e20.3 Visualizing and Analyzing Networks 567\u003c\/p\u003e \u003cp\u003e20.4 Social Data Metrics and Taxonomy 571\u003c\/p\u003e \u003cp\u003e20.5 Using Network Metrics in Prediction and Classification 576\u003c\/p\u003e \u003cp\u003e20.6 Collecting Social Network Data with RapidMiner 584\u003c\/p\u003e \u003cp\u003e20.7 Advantages and Disadvantages 584\u003c\/p\u003e \u003cp\u003eProblems 587\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Text Mining 589\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 589\u003c\/p\u003e \u003cp\u003e21.2 The Tabular Representation of Text: Term–Document Matrix and “Bag-of-Words’’ 590\u003c\/p\u003e \u003cp\u003e21.3 Bag-of-Words vs. Meaning Extraction at Document Level 592\u003c\/p\u003e \u003cp\u003e21.4 Preprocessing the Text 593\u003c\/p\u003e \u003cp\u003e21.5 Implementing Machine Learning Methods 602\u003c\/p\u003e \u003cp\u003e21.6 Example: Online Discussions on Autos and Electronics 602\u003c\/p\u003e \u003cp\u003e21.7 Example: Sentiment Analysis of Movie Reviews 607\u003c\/p\u003e \u003cp\u003e21.8 Summary 614\u003c\/p\u003e \u003cp\u003eProblems 615\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Responsible Data Science 617\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 617\u003c\/p\u003e \u003cp\u003e22.2 Unintentional Harm 618\u003c\/p\u003e \u003cp\u003e22.3 Legal Considerations 620\u003c\/p\u003e \u003cp\u003e22.4 Principles of Responsible Data Science 621\u003c\/p\u003e \u003cp\u003e22.5 A Responsible Data Science Framework 624\u003c\/p\u003e \u003cp\u003e22.6 Documentation Tools 628\u003c\/p\u003e \u003cp\u003e22.7 Example: Applying the RDS Framework to the COMPAS Example 631\u003c\/p\u003e \u003cp\u003e22.8 Summary 641\u003c\/p\u003e \u003cp\u003eProblems 643\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IX Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23 Cases 647\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Charles Book Club 647\u003c\/p\u003e \u003cp\u003e23.2 German Credit 654\u003c\/p\u003e \u003cp\u003e23.3 Tayko Software Cataloger 659\u003c\/p\u003e \u003cp\u003e23.4 Political Persuasion 663\u003c\/p\u003e \u003cp\u003e23.5 Taxi Cancellations 667\u003c\/p\u003e \u003cp\u003e23.6 Segmenting Consumers of Bath Soap 669\u003c\/p\u003e \u003cp\u003e23.7 Direct-Mail Fundraising 673\u003c\/p\u003e \u003cp\u003e23.8 Catalog Cross-Selling 676\u003c\/p\u003e \u003cp\u003e23.9 Time Series Case: Forecasting Public Transportation Demand 678\u003c\/p\u003e \u003cp\u003e23.10 Loan Approval 680\u003c\/p\u003e \u003cp\u003eReferences 683\u003c\/p\u003e \u003cp\u003eData Files Used in the Book 687\u003c\/p\u003e \u003cp\u003eIndex 689\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGalit Shmueli, PhD,\u003c\/b\u003e is Distinguished 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 Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAmit V. Deokar, PhD,\u003c\/b\u003e is Associate Dean of Undergraduate Programs and an Associate Professor of Management Information Systems at the Manning School of Business at University of Massachusetts Lowell. Since 2006, he has developed and taught courses in business analytics, with expertise in using the RapidMiner platform. He is an Association for Information Systems Distinguished Member Cum Laude.  \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\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 RapidMiner\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 seventh edition of \u003ci\u003eMachine Learning for Business Analytics\u003c\/i\u003e, and the first using RapidMiner software. This edition also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner\u003c\/li\u003e \u003cli\u003e Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years\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":47989548122341,"sku":"NP9781119828792","price":109.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119828792.jpg?v=1761784550","url":"https:\/\/k12savings.com\/products\/machine-learning-for-business-analytics-isbn-9781119828792","provider":"K12savings","version":"1.0","type":"link"}