What’s the Best Way to Handle Normalization Data for Consistent Results? or How Do You Approach Norma

Wow, didn’t expect so many great tips! Robust scaling and Quantile Transformer sound like exactly what I need for my current mess of a dataset.

Gonna try clipping + min-max too—seems like a simple fix for those rogue outliers.

Quick Q: Anyone have a favorite tool for *automating* normalization checks? Like, something that flags if the scaled data looks off?

(Also, big thanks for the sklearn reminders. I always forget about `Pipeline`.)