Normalization and Data Scaling in R

Data normalization and scaling are techniques used to standardize the range and distribution of the variables in a dataset. This is important because many machine learning algorithms use distance-based calculations, such as Euclidean distance, to compare samples and make predictions. If the features in a dataset have different scales and ranges, then some features may dominate others, causing the model to be biased toward those features.