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

Ugh, debugging normalization issues is the worst. One time, my pipeline silently failed because of NaN values *after* scaling.

Now I always:
1. Check for NaNs/missing values *first*
2. Plot distributions before/after
3. Use `Pipeline` in sklearn to avoid weird ordering bugs

Life got easier after that.