# Algorithms that would benefit from variable transformations?

1- Which algorithms would benefit from data that has been transformed, so that distributions of continuous variables resemble that of a normal distribution ?

2- What would be the benefits of transforming variables in such a way ?

2. Transforming to Gaussian makes the data symmetric and removes both heavy and long tails. That does a similar job of normalizing to $$[0,1]$$, without aggressively smashing the data into a bounded interval.