Normalization is a common feature engineering technique. However, this post used standardize(zscore) on the dataset before normalizing it.

I think that would result in losing some of the information in data.

What are the pros and cons of doing this?


2 Answers 2


Normalizing an already normalized dataset should not change anything unless for some reason a different normalization scheme is used.


Z-scores normalisation are a way to compare results from a test to a “normal” population and bring them to a same comparable scale. Advantages of ZScore can thus be:

$$ z\_score = \frac{x-\bar x}{\sigma} $$

The Z score normalisation has the following advantages:

  1. Z Score can be used to compare raw scores that are taken from different tests
  2. Z score takes into account both the mean value and the variability in a set of raw scores.

And the Disadvantages of Z score are:

  1. Z Score always assume a normal distribution.
  2. If the data is skewed, the distribution of the left and right of the origin line is not equal.

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