Data transformation techniques tame the dataset to a format that a machine learning algorithm expects as input and may even help the algorithm to learn faster and achieve better performance. It is also known as data munging or data wrangling. Standardization, for instance, assumes that data follows Gaussian distribution and transforms the values in such a way that the mean value is 0 and the deviation is 1, as follows:
Normalization, on the other hand, scales the values of attributes to a small, specified range, usually between 0 and 1:
Many machine learning toolboxes automatically normalize and standardize the data ...
