Data Mining and Business Analytics with R by Johannes Ledolter provides a comprehensive guide to analyzing large, high-dimensional data sets using the open-source software R. This book is essential for those looking to collect, analyze, and extract valuable information from complex datasets. It begins with fundamental concepts in linear regression and emphasizes the importance of model simplicity (parsimony) in statistical modeling. Key topics include penalty-based variable selection (LASSO), logistic regression, decision trees, clustering techniques, principal components analysis, partial least squares, and methods for analyzing text and network data.

The book offers a blend of theoretical foundations and practical computational skills, making it accessible to both beginners and advanced users. Each chapter includes detailed explanations and real-world applications, helping readers understand how to apply these concepts in their own work. Additionally, the author provides supplementary datasets and R code, enabling readers to replicate analyses and gain hands-on experience with the discussed techniques.

Published by Wiley on May 28, 2013, this volume consists of 368 pages designed to equip professionals with the tools necessary for effective data mining and business analytics. With its focus on practical utility and real-world relevance, Data Mining and Business Analytics with R is an invaluable resource for students, researchers, and practitioners in fields such as statistics, computer science, economics, and business management.