Should one normalize the frequency values when feeding it as an input to machine learning model?
As generic advice, any algorithm that uses distances might be affected by scaling or normalization. Thus, if you use one of them, you need to think about it twice and consider how this will alter your problem.

I will take the answer from the CrossValidated StackExchange since another user has already explained it there.


  If your variables are of incomparable units (e.g. height in cm and
  weight in kg) then you should standardize variables, of course. Even
  if variables are of the same units but show quite different variances
  it is still a good idea to standardize before K-means. You see,
  K-means clustering is "isotropic" in all directions of space and
  therefore tends to produce more or less round (rather than elongated)
  clusters. In this situation leaving variances unequal is equivalent to
  putting more weight on variables with smaller variance, so clusters
  will tend to be separated along variables with greater variance.


ttnphns's answer