Data preparation is the foundation of any successful machine learning project. This volume provides a comprehensive guide to cleaning, transforming, and splitting data for machine learning using R, including handling missing values, feature scaling, and stratified sampling. Practical examples and R code demonstrate how to optimize datasets for predictive modeling. The volume is essential for data scientists and machine learning practitioners seeking to build robust models.