lol i feel u. whats the point of pandas normalization when u can just eyeball it? but here’s the thing: some algorithms *hate* unbalanced data.
imagine u’re training a model where one feature ranges 0-1000 and another is 0-1. the big numbers will dominate, and the model won’t learn properly. normalization evens the playing field.
for quick checks, u can use `df.describe()` before/after to see how it changes. not always necessary, but super helpful for ML.
imagine u’re training a model where one feature ranges 0-1000 and another is 0-1. the big numbers will dominate, and the model won’t learn properly. normalization evens the playing field.
for quick checks, u can use `df.describe()` before/after to see how it changes. not always necessary, but super helpful for ML.