Data Mining and Business Analytics with R

<p>Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R 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.</p> <p>Highlighting both underlying concepts and practical computational skills, <i>Data Mining and Business Analytics with R</i> 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:</p> <p>• A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools</p> <p>• Illustrations of how to use the outlined concepts in real-world situations</p> <p>• Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials</p> <p>• Numerous exercises to help readers with computing skills and deepen their understanding of the material</p> <p><i>Data Mining and Business Analytics with R</i> 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.</p>