{"product_id":"machine-learning-for-business-analytics-9781119828792","title":"Machine Learning for Business Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eMachine Learning for Business Analytics\u003c\/b\u003e \u003cp\u003e\u003cb\u003eMachine learningalso known as data mining or data analyticsis 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\u003cli\u003e A\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword by Ravi Bapna xxi\u003c\/p\u003e \u003cp\u003ePreface to the RapidMiner 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\u003c\/b\u003e \u003cb\u003eIntroduction 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\u003c\/b\u003e \u003cb\u003eOverview 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\u003e\u003cb\u003ePART II DATA EXPLORATION AND DIMENSION REDUCTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 3\u003c\/b\u003e \u003cb\u003eData 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\u003c\/b\u003e \u003cb\u003eDimension 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\u003e\u003cb\u003ePART III PERFORMANCE EVALUATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 5\u003c\/b\u003e \u003cb\u003eEvaluating 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\u003e\u003cb\u003ePART IV PREDICTION AND CLASSIFICATION METHODS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 6\u003c\/b\u003e \u003cb\u003eMultiple 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\u003e\u003cb\u003eCHAPTER 7\u003c\/b\u003e \u003cb\u003e\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 \u003cb\u003e\u003ci\u003ek\u003c\/i\u003e\u003c\/b\u003e-NN for a Numerical Label 200\u003c\/p\u003e \u003cp\u003e7.3 Advantages and Shortcomings of \u003cb\u003e\u003ci\u003ek\u003c\/i\u003e\u003c\/b\u003e-NN Algorithms 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 8\u003c\/b\u003e \u003cb\u003eThe 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 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003eCHAPTER 9\u003c\/b\u003e \u003cb\u003eClassification 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\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\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\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\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\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\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\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 \u003ci\u003ek\u003c\/i\u003e-Means Algorithm 466\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\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 504\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\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 541\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\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 577\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\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\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\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 653\u003c\/p\u003e \u003cp\u003e23.3 Tayko Software Cataloger 658\u003c\/p\u003e \u003cp\u003e23.4 Political Persuasion 662\u003c\/p\u003e \u003cp\u003e23.5 Taxi Cancellations 665\u003c\/p\u003e \u003cp\u003e23.6 Segmenting Consumers of Bath Soap 667\u003c\/p\u003e \u003cp\u003e23.7 Direct-Mail Fundraising 670\u003c\/p\u003e \u003cp\u003e23.8 Catalog Cross-Selling 672\u003c\/p\u003e \u003cp\u003e23.9 Time Series Case: Forecasting Public Transportation Demand 673\u003c\/p\u003e \u003cp\u003e23.10 Loan Approval 675\u003c\/p\u003e \u003cp\u003eIndex 685\u003c\/p\u003e\n\u003c\/li\u003e\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407164842327,"sku":"9781119828792","price":96.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119828792.jpg?v=1730498397","url":"https:\/\/bookcurl.com\/products\/machine-learning-for-business-analytics-9781119828792","provider":"Book Curl","version":"1.0","type":"link"}