Following are the different techniques employed to achieve data normalization:-
Rescaling: Rescaling data is the process of multiplying each member of a data set by a constant term k, or changing each integer x to f(X), where f(x) = kx and k and x are both real values. The simplest of all approaches, rescaling (also known as "min-max normalization"), is calculated as:
{"detectHand":false}
This represents the rescaling factor for every data point x.
Mean Normalisation: In the transformation process, this approach employs the mean of the observations:
{"detectHand":false}
This represents the mean normalizing factor for every data point x.
Z-score Normalisation: This technique, also known as standardization, employs the Z-score or "standard score." SVM and logistic regression are two examples of machine learning algorithms that utilise it:
{"detectHand":false}
This represents the Z-score.
