{"product_id":"data-mining-and-business-analytics-with-r-hardback-9781118447147","title":"Data Mining and Business Analytics with R (Hardback) 9781118447147","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Mining and Business Analytics with R\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJohannes Ledolter (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781118447147, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 June 2013\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e23.6 x 15.8 x 2.5 cm, 0.68 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, 1 January 2014)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. \u003ci\u003eData Mining and Business Analytics with R\u003c\/i\u003e utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.\u003c\/p\u003e \u003cp\u003eHighlighting both underlying concepts and practical computational skills, \u003ci\u003eData Mining and Business Analytics with R\u003c\/i\u003e begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA thorough discussion and extensive demonstration of the theory behind the most useful data mining tools\u003c\/li\u003e \u003cli\u003eIllustrations of how to use the outlined concepts in real-world situations\u003c\/li\u003e \u003cli\u003eReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials\u003c\/li\u003e \u003cli\u003eNumerous exercises to help readers with computing skills and deepen their understanding of the material\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Mining and Business Analytics with R\u003c\/i\u003e is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReference 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Processing the Information and Getting to Know Your Data 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Example 1: 2006 Birth Data 7\u003c\/p\u003e \u003cp\u003e2.2 Example 2: Alumni Donations 17\u003c\/p\u003e \u003cp\u003e2.3 Example 3: Orange Juice 31\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Standard Linear Regression 40\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Estimation in R 43\u003c\/p\u003e \u003cp\u003e3.2 Example 1: Fuel Efficiency of Automobiles 43\u003c\/p\u003e \u003cp\u003e3.3 Example 2: Toyota Used-Car Prices 47\u003c\/p\u003e \u003cp\u003eAppendix 3.A The Effects of Model Overfitting on the Average Mean Square Error of the Regression Prediction 53\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Local Polynomial Regression: a Nonparametric Regression Approach 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Model Selection 56\u003c\/p\u003e \u003cp\u003e4.2 Application to Density Estimation and the Smoothing of Histograms 58\u003c\/p\u003e \u003cp\u003e4.3 Extension to the Multiple Regression Model 58\u003c\/p\u003e \u003cp\u003e4.4 Examples and Software 58\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Importance of Parsimony in Statistical Modeling 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 How Do We Guard Against False Discovery 67\u003c\/p\u003e \u003cp\u003eReferences 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Penalty-Based Variable Selection in Regression Models with Many Parameters (LASSO) 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Example 1: Prostate Cancer 74\u003c\/p\u003e \u003cp\u003e6.2 Example 2: Orange Juice 78\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Logistic Regression 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Building a Linear Model for Binary Response Data 83\u003c\/p\u003e \u003cp\u003e7.2 Interpretation of the Regression Coefficients in a Logistic Regression Model 85\u003c\/p\u003e \u003cp\u003e7.3 Statistical Inference 85\u003c\/p\u003e \u003cp\u003e7.4 Classification of New Cases 86\u003c\/p\u003e \u003cp\u003e7.5 Estimation in R 87\u003c\/p\u003e \u003cp\u003e7.6 Example 1: Death Penalty Data 87\u003c\/p\u003e \u003cp\u003e7.7 Example 2: Delayed Airplanes 92\u003c\/p\u003e \u003cp\u003e7.8 Example 3: Loan Acceptance 100\u003c\/p\u003e \u003cp\u003e7.9 Example 4: German Credit Data 103\u003c\/p\u003e \u003cp\u003eReferences 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Binary Classification, Probabilities, and Evaluating Classification Performance 108\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Binary Classification 108\u003c\/p\u003e \u003cp\u003e8.2 Using Probabilities to Make Decisions 108\u003c\/p\u003e \u003cp\u003e8.3 Sensitivity and Specificity 109\u003c\/p\u003e \u003cp\u003e8.4 Example: German Credit Data 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Classification Using a Nearest Neighbor Analysis 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The k-Nearest Neighbor Algorithm 116\u003c\/p\u003e \u003cp\u003e9.2 Example 1: Forensic Glass 117\u003c\/p\u003e \u003cp\u003e9.3 Example 2: German Credit Data 122\u003c\/p\u003e \u003cp\u003eReference 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. The Na¨ýve Bayesian Analysis: a Model for Predicting a Categorical Response from Mostly Categorical\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePredictor Variables 126\u003c\/p\u003e \u003cp\u003e10.1 Example: Delayed Airplanes 127\u003c\/p\u003e \u003cp\u003eReference 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Multinomial Logistic Regression 132\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Computer Software 134\u003c\/p\u003e \u003cp\u003e11.2 Example 1: Forensic Glass 134\u003c\/p\u003e \u003cp\u003e11.3 Example 2: Forensic Glass Revisited 141\u003c\/p\u003e \u003cp\u003eAppendix 11.A Specification of a Simple Triplet Matrix 147\u003c\/p\u003e \u003cp\u003eReferences 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. More on Classification and a Discussion on Discriminant Analysis 150\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Fisher’s Linear Discriminant Function 153\u003c\/p\u003e \u003cp\u003e12.2 Example 1: German Credit Data 154\u003c\/p\u003e \u003cp\u003e12.3 Example 2: Fisher Iris Data 156\u003c\/p\u003e \u003cp\u003e12.4 Example 3: Forensic Glass Data 157\u003c\/p\u003e \u003cp\u003e12.5 Example 4: MBA Admission Data 159\u003c\/p\u003e \u003cp\u003eReference 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Decision Trees 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Example 1: Prostate Cancer 167\u003c\/p\u003e \u003cp\u003e13.2 Example 2: Motorcycle Acceleration 179\u003c\/p\u003e \u003cp\u003e13.3 Example 3: Fisher Iris Data Revisited 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 R Packages for Tree Construction 185\u003c\/p\u003e \u003cp\u003e14.2 Chi-Square Automatic Interaction Detection (CHAID) 186\u003c\/p\u003e \u003cp\u003e14.3 Ensemble Methods: Bagging, Boosting, and Random Forests 188\u003c\/p\u003e \u003cp\u003e14.4 Support Vector Machines (SVM) 192\u003c\/p\u003e \u003cp\u003e14.5 Neural Networks 192\u003c\/p\u003e \u003cp\u003e14.6 The R Package Rattle: A Useful Graphical User Interface for Data Mining 193\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Clustering 196\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 k-Means Clustering 196\u003c\/p\u003e \u003cp\u003e15.2 Another Way to Look at Clustering: Applying the Expectation-Maximization (EM) Algorithm to Mixtures of Normal Distributions 204\u003c\/p\u003e \u003cp\u003e15.3 Hierarchical Clustering Procedures 212\u003c\/p\u003e \u003cp\u003eReferences 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Market Basket Analysis: Association Rules and Lift 220\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Example 1: Online Radio 222\u003c\/p\u003e \u003cp\u003e16.2 Example 2: Predicting Income 227\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Dimension Reduction: Factor Models and Principal Components 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Example 1: European Protein Consumption 238\u003c\/p\u003e \u003cp\u003e17.2 Example 2: Monthly US Unemployment Rates 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. Reducing the Dimension in Regressions with Multicollinear Inputs: Principal Components Regression and Partial Least Squares 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Three Examples 249\u003c\/p\u003e \u003cp\u003eReferences 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. Text as Data: Text Mining and Sentiment Analysis 258\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Inverse Multinomial Logistic Regression 259\u003c\/p\u003e \u003cp\u003e19.2 Example 1: Restaurant Reviews 261\u003c\/p\u003e \u003cp\u003e19.3 Example 2: Political Sentiment 266\u003c\/p\u003e \u003cp\u003eAppendix 19.A Relationship Between the Gentzkow Shapiro Estimate of “Slant” and Partial Least Squares 268\u003c\/p\u003e \u003cp\u003eReferences 271\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20. Network Data 272\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Example 1: Marriage and Power in Fifteenth Century Florence 274\u003c\/p\u003e \u003cp\u003e20.2 Example 2: Connections in a Friendship Network 278\u003c\/p\u003e \u003cp\u003eReferences 292\u003c\/p\u003e \u003cp\u003eAppendix A: Exercises 293\u003c\/p\u003e \u003cp\u003eExercise 1 294\u003c\/p\u003e \u003cp\u003eExercise 2 294\u003c\/p\u003e \u003cp\u003eExercise 3 296\u003c\/p\u003e \u003cp\u003eExercise 4 298\u003c\/p\u003e \u003cp\u003eExercise 5 299\u003c\/p\u003e \u003cp\u003eExercise 6 300\u003c\/p\u003e \u003cp\u003eExercise 7 301\u003c\/p\u003e \u003cp\u003eAppendix B: References 338\u003c\/p\u003e \u003cp\u003eIndex 341\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52472120443160,"sku":"9781118447147","price":80.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781118447147.jpg?v=1785717724","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-mining-and-business-analytics-with-r-hardback-9781118447147","provider":"Freshly Printed Books","version":"1.0","type":"link"}