{"product_id":"data-mining-for-business-analytics-isbn-9781119549840","title":"Data Mining for Business Analytics","description":"\u003cp\u003e\u003cb\u003e\u003ci\u003eData Mining for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e presents an applied approach to data mining concepts and methods, using Python software for illustration\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReaders will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.\u003c\/p\u003e \u003cp\u003eThis is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process\u003c\/li\u003e \u003cli\u003eA new section on ethical issues in data mining\u003c\/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003eMore than a dozen case studies demonstrating applications for the data mining techniques described\u003c\/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Mining for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.\u003c\/p\u003e \u003cp\u003e“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”\u003c\/p\u003e \u003cp\u003e—Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book \u003ci\u003eAn Introduction to Statistical Learning, with Applications in R \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eForeword by \u003ci\u003eGareth James\u003c\/i\u003e xix\u003c\/p\u003e \u003cp\u003eForeword by \u003ci\u003eRavi Bapna\u003c\/i\u003e xxi\u003c\/p\u003e \u003cp\u003ePreface to the 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\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 Data Mining? 5\u003c\/p\u003e \u003cp\u003e1.3 Data Mining 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 9\u003c\/p\u003e \u003cp\u003e1.8 Road Maps to This Book 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2\u003c\/b\u003e \u003cb\u003eOverview of the Data Mining Process 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 Core Ideas in Data Mining 16\u003c\/p\u003e \u003cp\u003e2.3 The Steps in Data Mining 19\u003c\/p\u003e \u003cp\u003e2.4 Preliminary Steps 21\u003c\/p\u003e \u003cp\u003e2.5 Predictive Power and Overfitting 34\u003c\/p\u003e \u003cp\u003e2.6 Building a Predictive Model 40\u003c\/p\u003e \u003cp\u003e2.7 Using Python for Data Mining on a Local Machine 44\u003c\/p\u003e \u003cp\u003e2.8 Automating Data Mining Solutions 45\u003c\/p\u003e \u003cp\u003e2.9 Ethical Practice in Data Mining 47\u003c\/p\u003e \u003cp\u003eProblems 56\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 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 61\u003c\/p\u003e \u003cp\u003e3.2 Data Examples 64\u003c\/p\u003e \u003cp\u003e3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 65\u003c\/p\u003e \u003cp\u003e3.4 Multidimensional Visualization 74\u003c\/p\u003e \u003cp\u003e3.5 Specialized Visualizations 88\u003c\/p\u003e \u003cp\u003e3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 93\u003c\/p\u003e \u003cp\u003eProblems 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4\u003c\/b\u003e \u003cb\u003eDimension Reduction 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 100\u003c\/p\u003e \u003cp\u003e4.2 Curse of Dimensionality 100\u003c\/p\u003e \u003cp\u003e4.3 Practical Considerations 100\u003c\/p\u003e \u003cp\u003e4.4 Data Summaries 102\u003c\/p\u003e \u003cp\u003e4.5 Correlation Analysis 105\u003c\/p\u003e \u003cp\u003e4.6 Reducing the Number of Categories in Categorical Variables 106\u003c\/p\u003e \u003cp\u003e4.7 Converting a Categorical Variable to a Numerical Variable 108\u003c\/p\u003e \u003cp\u003e4.8 Principal Components Analysis 108\u003c\/p\u003e \u003cp\u003e4.9 Dimension Reduction Using Regression Models 119\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\u003c\/b\u003e \u003cb\u003eEvaluating Predictive Performance 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 126\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 144\u003c\/p\u003e \u003cp\u003e5.5 Oversampling 149\u003c\/p\u003e \u003cp\u003eProblems 155\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 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 162\u003c\/p\u003e \u003cp\u003e6.2 Explanatory vs. Predictive Modeling 162\u003c\/p\u003e \u003cp\u003e6.3 Estimating the Regression Equation and Prediction 164\u003c\/p\u003e \u003cp\u003e6.4 Variable Selection in Linear Regression 169\u003c\/p\u003e \u003cp\u003eAppendix: Using Statmodels 179\u003c\/p\u003e \u003cp\u003eProblems 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7\u003c\/b\u003e \u003cb\u003e\u003ci\u003ek\u003c\/i\u003e-Nearest Neighbors (\u003ci\u003ek\u003c\/i\u003eNN) 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The \u003ci\u003ek\u003c\/i\u003e-NN Classifier (Categorical Outcome) 185\u003c\/p\u003e \u003cp\u003e7.2 \u003cb\u003e\u003ci\u003ek\u003c\/i\u003e\u003c\/b\u003e-NN for a Numerical Outcome 193\u003c\/p\u003e \u003cp\u003e7.3 Advantages and Shortcomings of \u003cb\u003e\u003ci\u003ek\u003c\/i\u003e\u003c\/b\u003e-NN Algorithms 195\u003c\/p\u003e \u003cp\u003eProblems 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8\u003c\/b\u003e \u003cb\u003eThe Naive Bayes Classifier 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 199\u003c\/p\u003e \u003cp\u003eExample 1: Predicting Fraudulent Financial Reporting 201\u003c\/p\u003e \u003cp\u003e8.2 Applying the Full (Exact) Bayesian Classifier 201\u003c\/p\u003e \u003cp\u003e8.3 Advantages and Shortcomings of the Naive Bayes Classifier 210\u003c\/p\u003e \u003cp\u003eProblems 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9\u003c\/b\u003e \u003cb\u003eClassification and Regression Trees 217\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 218\u003c\/p\u003e \u003cp\u003e9.2 Classification Trees 220\u003c\/p\u003e \u003cp\u003e9.3 Evaluating the Performance of a Classification Tree 228\u003c\/p\u003e \u003cp\u003e9.4 Avoiding Overfitting 232\u003c\/p\u003e \u003cp\u003e9.5 Classification Rules from Trees 238\u003c\/p\u003e \u003cp\u003e9.6 Classification Trees for More Than Two Classes 239\u003c\/p\u003e \u003cp\u003e9.7 Regression Trees 239\u003c\/p\u003e \u003cp\u003e9.8 Improving Prediction: Random Forests and Boosted Trees 243\u003c\/p\u003e \u003cp\u003e9.9 Advantages and Weaknesses of a Tree 246\u003c\/p\u003e \u003cp\u003eProblems 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10\u003c\/b\u003e \u003cb\u003eLogistic Regression 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 252\u003c\/p\u003e \u003cp\u003e10.2 The Logistic Regression Model 253\u003c\/p\u003e \u003cp\u003e10.3 Example: Acceptance of Personal Loan 255\u003c\/p\u003e \u003cp\u003e10.4 Evaluating Classification Performance 261\u003c\/p\u003e \u003cp\u003e10.5 Logistic Regression for Multi-class Classification 264\u003c\/p\u003e \u003cp\u003e10.6 Example of Complete Analysis: Predicting Delayed Flights 269\u003c\/p\u003e \u003cp\u003eAppendix: Using Statmodels 278\u003c\/p\u003e \u003cp\u003eProblems 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11\u003c\/b\u003e \u003cb\u003eNeural Nets 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 284\u003c\/p\u003e \u003cp\u003e11.2 Concept and Structure of a Neural Network 284\u003c\/p\u003e \u003cp\u003e11.3 Fitting a Network to Data 285\u003c\/p\u003e \u003cp\u003e11.4 Required User Input 297\u003c\/p\u003e \u003cp\u003e11.5 Exploring the Relationship Between Predictors and Outcome 299\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning 299\u003c\/p\u003e \u003cp\u003e11.7 Advantages and Weaknesses of Neural Networks 305\u003c\/p\u003e \u003cp\u003eProblems 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Discriminant Analysis 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 310\u003c\/p\u003e \u003cp\u003e12.2 Distance of a Record from a Class 311\u003c\/p\u003e \u003cp\u003e12.3 Fisher’s Linear Classification Functions 314\u003c\/p\u003e \u003cp\u003e12.4 Classification Performance of Discriminant Analysis 317\u003c\/p\u003e \u003cp\u003e12.5 Prior Probabilities 318\u003c\/p\u003e \u003cp\u003e12.6 Unequal Misclassification Costs 319\u003c\/p\u003e \u003cp\u003e12.7 Classifying More Than Two Classes 319\u003c\/p\u003e \u003cp\u003e12.8 Advantages and Weaknesses 322\u003c\/p\u003e \u003cp\u003eProblems 324\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13\u003c\/b\u003e \u003cb\u003eCombining Methods: Ensembles and Uplift Modeling 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Ensembles 328\u003c\/p\u003e \u003cp\u003e13.2 Uplift (Persuasion) Modeling 334\u003c\/p\u003e \u003cp\u003e13.3 Summary 340\u003c\/p\u003e \u003cp\u003eProblems 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Mining Relationships among Records\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14\u003c\/b\u003e \u003cb\u003eAssociation Rules and Collaborative Filtering 345\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Association Rules 346\u003c\/p\u003e \u003cp\u003e14.2 Collaborative Filtering 357\u003c\/p\u003e \u003cp\u003e14.3 Summary 368\u003c\/p\u003e \u003cp\u003eProblems 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15\u003c\/b\u003e \u003cb\u003eCluster Analysis 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 376\u003c\/p\u003e \u003cp\u003e15.2 Measuring Distance Between Two Records 379\u003c\/p\u003e \u003cp\u003e15.3 Measuring Distance Between Two Clusters 385\u003c\/p\u003e \u003cp\u003e15.4 Hierarchical (Agglomerative) Clustering 387\u003c\/p\u003e \u003cp\u003e15.5 Non-Hierarchical Clustering: The \u003ci\u003ek\u003c\/i\u003e-Means Algorithm 395\u003c\/p\u003e \u003cp\u003eProblems 401\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI Forecasting Time Series\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16\u003c\/b\u003e \u003cb\u003eHandling Time Series 407\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 408\u003c\/p\u003e \u003cp\u003e16.2 Descriptive vs. Predictive Modeling 409\u003c\/p\u003e \u003cp\u003e16.3 Popular Forecasting Methods in Business 409\u003c\/p\u003e \u003cp\u003e16.4 Time Series Components 410\u003c\/p\u003e \u003cp\u003e16.5 Data-Partitioning and Performance Evaluation 415\u003c\/p\u003e \u003cp\u003eProblems 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17\u003c\/b\u003e \u003cb\u003eRegression-Based Forecasting 423\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 A Model with Trend 424\u003c\/p\u003e \u003cp\u003e17.2 A Model with Seasonality 429\u003c\/p\u003e \u003cp\u003e17.3 A Model with Trend and Seasonality 432\u003c\/p\u003e \u003cp\u003e17.4 Autocorrelation and ARIMA Models 433\u003c\/p\u003e \u003cp\u003eProblems 442\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18\u003c\/b\u003e \u003cb\u003eSmoothing Methods 451\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 452\u003c\/p\u003e \u003cp\u003e18.2 Moving Average 452\u003c\/p\u003e \u003cp\u003e18.3 Simple Exponential Smoothing 457\u003c\/p\u003e \u003cp\u003e18.4 Advanced Exponential Smoothing 460\u003c\/p\u003e \u003cp\u003eProblems 464\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VII Data Analytics\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19\u003c\/b\u003e \u003cb\u003eSocial Network Analytics 473\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 473\u003c\/p\u003e \u003cp\u003e19.2 Directed vs. Undirected Networks 475\u003c\/p\u003e \u003cp\u003e19.3 Visualizing and Analyzing Networks 476\u003c\/p\u003e \u003cp\u003e19.4 Social Data Metrics and Taxonomy 480\u003c\/p\u003e \u003cp\u003e19.5 Using Network Metrics in Prediction and Classification 485\u003c\/p\u003e \u003cp\u003e19.6 Collecting Social Network Data with Python 491\u003c\/p\u003e \u003cp\u003e19.7 Advantages and Disadvantages 491\u003c\/p\u003e \u003cp\u003eProblems 494\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20\u003c\/b\u003e \u003cb\u003eText Mining 495\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 496\u003c\/p\u003e \u003cp\u003e20.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words’’ 496\u003c\/p\u003e \u003cp\u003e20.3 Bag-of-Words vs. Meaning Extraction at Document Level 497\u003c\/p\u003e \u003cp\u003e20.4 Preprocessing the Text 498\u003c\/p\u003e \u003cp\u003e20.5 Implementing Data Mining Methods 506\u003c\/p\u003e \u003cp\u003e20.6 Example: Online Discussions on Autos and Electronics 506\u003c\/p\u003e \u003cp\u003e20.7 Summary 510\u003c\/p\u003e \u003cp\u003eProblems 511\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VIII Cases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21\u003c\/b\u003e \u003cb\u003eCases 515\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Charles Book Club 515\u003c\/p\u003e \u003cp\u003e21.2 German Credit 522\u003c\/p\u003e \u003cp\u003e21.3 Tayko Software Cataloger 527\u003c\/p\u003e \u003cp\u003e21.4 Political Persuasion 531\u003c\/p\u003e \u003cp\u003e21.5 Taxi Cancellations 535\u003c\/p\u003e \u003cp\u003e21.6 Segmenting Consumers of Bath Soap 537\u003c\/p\u003e \u003cp\u003e21.7 Direct-Mail Fundraising 541\u003c\/p\u003e \u003cp\u003e21.8 Catalog Cross-Selling 544\u003c\/p\u003e \u003cp\u003e21.9 Time Series Case: Forecasting Public Transportation Demand 546\u003c\/p\u003e \u003cp\u003eReferences 549\u003c\/p\u003e \u003cp\u003eData Files Used in the Book 551\u003c\/p\u003e \u003cp\u003ePython Utilities Functions 555\u003c\/p\u003e \u003cp\u003eIndex 565\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGALIT SHMUELI, P\u003csmall\u003eH\u003c\/small\u003eD\u003c\/b\u003e, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 100 publications including books. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePETER C. BRUCE\u003c\/b\u003e is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of \u003ci\u003eIntroductory Statistics and Analytics: A Resampling Perspective\u003c\/i\u003e (Wiley) and co-author of \u003ci\u003ePractical Statistics for Data Scientists: 50 Essential Concepts\u003c\/i\u003e (O'Reilly). \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePETER GEDECK, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Senior Data Scientist at Collaborative Drug Discovery, where he helps develop cloud-based software to manage the huge amount of data involved in the drug discovery process. He also teaches data mining at Statistics.com. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNITIN R. PATEL, PhD,\u003c\/b\u003e is cofounder and board member of Cytel Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also 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\u003e\u003ci\u003eData Mining for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e\u003c\/b\u003e\u003cb\u003e presents an applied approach to data mining concepts and methods, using Python software for illustration\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eReaders will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. \u003c\/p\u003e\u003cp\u003eThis is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eA new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process\u003c\/li\u003e \u003cli\u003eA new section on ethical issues in data mining\u003c\/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003eMore than a dozen case studies demonstrating applications for the data mining techniques described\u003c\/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Mining for Business Analytics: Concepts, Techniques, and Applications in Python\u003c\/i\u003e is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. \u003c\/p\u003e\u003cp\u003e\"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.\" \u003cb\u003e—GARETH M. JAMES,\u003c\/b\u003e University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book \u003ci\u003eAn Introduction to Statistical Learning, with Applications in R\u003c\/i\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989024751845,"sku":"NP9781119549840","price":110.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119549840.jpg?v=1761782485","url":"https:\/\/k12savings.com\/es\/products\/data-mining-for-business-analytics-isbn-9781119549840","provider":"K12savings","version":"1.0","type":"link"}