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Answer» 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:
x'=x-min(x)max(x)-min(x){"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:
x'=x-average(x)max(x)-min(x){"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:
z=x-μσ{"detectHand":false}
This represents the Z-score.
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