{"product_id":"machine-learning-for-business-analytics-isbn-9781119903833","title":"Machine Learning for Business Analytics","description":"\u003cb\u003eMACHINE LEARNING FOR BUSINESS ANALYTICS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eAn up-to-date introduction to a market-leading platform for data analysis and machine learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,\u003c\/i\u003e 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,\u003c\/i\u003e 2nd ed. readers will also find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUpdated material which improves the book’s usefulness as a reference for professionals beyond the classroom\u003c\/li\u003e \u003cli\u003eFour new chapters, covering topics including Text Mining and Responsible Data Science\u003c\/li\u003e \u003cli\u003eAn updated companion website with data sets and other instructor resources: www.jmp.com\/dataminingbook\u003c\/li\u003e \u003cli\u003eA guide to JMP Pro's new features and enhanced functionality\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,\u003c\/i\u003e 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.\u003c\/p\u003e \u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface xx\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Preliminaries\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 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 6\u003c\/p\u003e \u003cp\u003e1.5 Data Science 7\u003c\/p\u003e \u003cp\u003e1.6 Why Are There So Many Different Methods? 8\u003c\/p\u003e \u003cp\u003e1.7 Terminology and Notation 8\u003c\/p\u003e \u003cp\u003e1.8 Road Maps to This Book 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 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\u003e2.3 The Steps in A Machine Learning Project 21\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 22\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 29\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model with JMP Pro 34\u003c\/p\u003e \u003cp\u003e2.7 Using JMP Pro for Machine Learning 42\u003c\/p\u003e \u003cp\u003e2.8 Automating Machine Learning Solutions 43\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Machine Learning 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Data Exploration and Dimension Reduction\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data Visualization 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 59\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 61\u003c\/p\u003e \u003cp\u003e3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 62\u003c\/p\u003e \u003cp\u003e3.4 Multidimensional Visualization 70\u003c\/p\u003e \u003cp\u003e3.5 Specialized Visualizations 82\u003c\/p\u003e \u003cp\u003e3.6 Summary: Major Visualizations and Operations, According to Machine Learning Goal 87\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Dimension Reduction 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 91\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 92\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Performance Evaluation\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Evaluating Predictive Performance 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 118\u003c\/p\u003e \u003cp\u003e5.2 Evaluating Predictive Performance 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Prediction and Classification Methods\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Multiple Linear Regression 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 147\u003c\/p\u003e \u003cp\u003e6.2 Explanatory vs. Predictive Modeling 148\u003c\/p\u003e \u003cp\u003e6.3 Estimating the Regression Equation and Prediction 149\u003c\/p\u003e \u003cp\u003e6.4 Variable Selection in Linear Regression 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 k-Nearest Neighbors (k-NN) 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The k-NN Classifier (Categorical Outcome) 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Naive Bayes Classifier 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Classification and Regression Trees 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 206\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 207\u003c\/p\u003e \u003cp\u003e9.3 Growing a Tree for Riding Mowers Example 210\u003c\/p\u003e \u003cp\u003e9.4 Evaluating the Performance of a Classification Tree 215\u003c\/p\u003e \u003cp\u003e9.5 Avoiding Overfitting 219\u003c\/p\u003e \u003cp\u003e9.6 Classification Rules from Trees 222\u003c\/p\u003e \u003cp\u003e9.7 Classification Trees for More Than Two Classes 224\u003c\/p\u003e \u003cp\u003e9.8 Regression Trees 224\u003c\/p\u003e \u003cp\u003e9.9 Advantages and Weaknesses of a Single Tree 227\u003c\/p\u003e \u003cp\u003e9.10 Improving Prediction: Random Forests and Boosted Trees 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Logistic Regression 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 237\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 239\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 240\u003c\/p\u003e \u003cp\u003e10.4 Evaluating Classification Performance 247\u003c\/p\u003e \u003cp\u003e10.5 Variable Selection 249\u003c\/p\u003e \u003cp\u003e10.6 Logistic Regression for Multi-class Classification 250\u003c\/p\u003e \u003cp\u003e10.7 Example of Complete Analysis: Predicting Delayed Flights 253\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Neural Nets 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 267\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 268\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 269\u003c\/p\u003e \u003cp\u003e11.4 User Input in JMP Pro 282\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Outcome 284\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 285\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Discriminant Analysis 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 293\u003c\/p\u003e \u003cp\u003e12.2 Distance of an Observation from a Class 295\u003c\/p\u003e \u003cp\u003e12.3 From Distances to Propensities and Classifications 297\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 300\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 301\u003c\/p\u003e \u003cp\u003e12.6 Classifying More Than Two Classes 303\u003c\/p\u003e \u003cp\u003e12.7 Advantages and Weaknesses 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Generating, Comparing, and Combining Multiple Models 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Ensembles 311\u003c\/p\u003e \u003cp\u003e13.2 Automated Machine Learning (AutoML) 317\u003c\/p\u003e \u003cp\u003e13.3 Summary 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Intervention and User Feedback\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Interventions: Experiments, Uplift Models, and Reinforcement Learning 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 327\u003c\/p\u003e \u003cp\u003e14.2 A\/B Testing 328\u003c\/p\u003e \u003cp\u003e14.3 Uplift (Persuasion) Modeling 333\u003c\/p\u003e \u003cp\u003e14.4 Reinforcement Learning 340\u003c\/p\u003e \u003cp\u003e14.5 Summary 344\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Mining Relationships Among Records\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Association Rules and Collaborative Filtering 349\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Association Rules 349\u003c\/p\u003e \u003cp\u003e15.2 Collaborative Filtering 362\u003c\/p\u003e \u003cp\u003e15.3 Summary 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Cluster Analysis 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 375\u003c\/p\u003e \u003cp\u003e16.2 Measuring Distance Between Two Records 378\u003c\/p\u003e \u003cp\u003e16.3 Measuring Distance Between Two Clusters 383\u003c\/p\u003e \u003cp\u003e16.4 Hierarchical (Agglomerative) Clustering 385\u003c\/p\u003e \u003cp\u003e16.5 Nonhierarchical Clustering: The K-Means Algorithm 394\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII Forecasting Time Series\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Handling Time Series 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 409\u003c\/p\u003e \u003cp\u003e17.2 Descriptive vs. Predictive Modeling 410\u003c\/p\u003e \u003cp\u003e17.3 Popular Forecasting Methods in Business 411\u003c\/p\u003e \u003cp\u003e17.4 Time Series Components 411\u003c\/p\u003e \u003cp\u003e17.5 Data Partitioning and Performance Evaluation 415\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Regression-Based Forecasting 423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 A Model with Trend 424\u003c\/p\u003e \u003cp\u003e18.2 A Model with Seasonality 430\u003c\/p\u003e \u003cp\u003e18.3 A Model with Trend and Seasonality 433\u003c\/p\u003e \u003cp\u003e18.4 Autocorrelation and ARIMA Models 433\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Smoothing and Deep Learning Methods for Forecasting 455\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 455\u003c\/p\u003e \u003cp\u003e19.2 Moving Average 456\u003c\/p\u003e \u003cp\u003e19.3 Simple Exponential Smoothing 461\u003c\/p\u003e \u003cp\u003e19.4 Advanced Exponential Smoothing 465\u003c\/p\u003e \u003cp\u003e19.5 Deep Learning for Forecasting 470\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Text Mining 483\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 483\u003c\/p\u003e \u003cp\u003e20.2 The Tabular Representation of Text: Document–Term Matrix and \"Bag-of-Words\" 484\u003c\/p\u003e \u003cp\u003e20.3 Bag-of-Words vs. Meaning Extraction at Document Level 486\u003c\/p\u003e \u003cp\u003e20.4 Preprocessing the Text 486\u003c\/p\u003e \u003cp\u003e20.5 Implementing Machine Learning Methods 492\u003c\/p\u003e \u003cp\u003e20.6 Example: Online Discussions on Autos and Electronics 492\u003c\/p\u003e \u003cp\u003e20.7 Example: Sentiment Analysis of Movie Reviews 500\u003c\/p\u003e \u003cp\u003e20.8 Summary 502\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Responsible Data Science 505\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 505\u003c\/p\u003e \u003cp\u003e21.2 Unintentional Harm 506\u003c\/p\u003e \u003cp\u003e21.3 Legal Considerations 508\u003c\/p\u003e \u003cp\u003e21.4 Principles of Responsible Data Science 508\u003c\/p\u003e \u003cp\u003e21.5 A Responsible Data Science Framework 511\u003c\/p\u003e \u003cp\u003e21.6 Documentation Tools 514\u003c\/p\u003e \u003cp\u003e21.7 Example: Applying the RDS Framework to the COMPAS Example 517\u003c\/p\u003e \u003cp\u003e21.8 Summary 526\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IX Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Cases 533\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Charles Book Club 533\u003c\/p\u003e \u003cp\u003e22.2 German Credit 541\u003c\/p\u003e \u003cp\u003e22.3 Tayko Software Cataloger 545\u003c\/p\u003e \u003cp\u003e22.4 Political Persuasion 548\u003c\/p\u003e \u003cp\u003e22.5 Taxi Cancellations 552\u003c\/p\u003e \u003cp\u003e22.6 Segmenting Consumers of Bath Soap 554\u003c\/p\u003e \u003cp\u003e22.7 Catalog Cross-Selling 557\u003c\/p\u003e \u003cp\u003e22.8 Direct-Mail Fundraising 559\u003c\/p\u003e \u003cp\u003e22.9 Time Series Case: Forecasting Public Transportation Demand 562\u003c\/p\u003e \u003cp\u003e22.10 Loan Approval 564\u003c\/p\u003e \u003cp\u003eIndex 573\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. 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\u003eMia L. Stephens, M.S.\u003c\/b\u003e is an Advisory Product Manager with JMP, driving the product vision and roadmaps for JMP and JMP Pro.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMuralidhara Anandamurthy, PhD\u003c\/b\u003e is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eNitin R. Patel, PhD\u003c\/b\u003e is cofounder and lead researcher at Cytel Inc. He is also a Fellow of the American Statistical Association and has served as a visiting professor at the Massachusetts Institute of Technology and Harvard University, among others.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAn up-to-date introduction to a market-leading platform for data analysis and machine learning\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed.\u003c\/i\u003e offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed.\u003c\/i\u003e readers will also find:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUpdated material which improves the book’s usefulness as a reference for professionals beyond the classroom\u003c\/li\u003e \u003cli\u003eFour new chapters, covering topics including Text Mining and Responsible Data Science\u003c\/li\u003e \u003cli\u003eAn updated companion website with data sets and other instructor resources: www.jmp.com\/dataminingbook\u003c\/li\u003e \u003cli\u003eA guide to JMP Pro's new features and enhanced functionality\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro,\u003c\/i\u003e 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548187877,"sku":"NP9781119903833","price":140.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119903833.jpg?v=1761784551","url":"https:\/\/k12savings.com\/products\/machine-learning-for-business-analytics-isbn-9781119903833","provider":"K12savings","version":"1.0","type":"link"}