\"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."- Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani), of the best-selling book "An Introduction to Statistical Learning, with Applications in R Incorporating an innovative focus on data visualization and time series forecasting, Data Mining for Business Analytics supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of the freely-available R software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods. The book includes discussions of R subroutines, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to expand their comprehension of the presented material. Over a dozen cases that require use of the different data mining techniques are introduced, and a related Web site features over two dozen data sets, exercise solutions, PowerPoint slides, and case solutions. Modern topics include text analytics, recommender systems, social network analysis, getting data from a database into the analytics process, and scoring and employing the results of an analysis to a database.

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