Min-Max Normalization

Features arrive on wildly different scales — age in years, income in tens of thousands — and any model that measures distance or shares one learning rate will quietly let the big one dominate. Min-max scaling squeezes everything onto [0, 1]:

Task: write min_max_normalize(values) returning the scaled list, rounded to 4 decimal places.

• The smallest value becomes exactly 0.0 and the largest exactly 1.0.
• If every value is identical there's no range to divide by — return all zeros rather than dividing by zero.
• An empty list returns an empty list.

This is the scaler that outliers punish hardest. One value ten times larger than the rest compresses everything else into a sliver near zero, which is the usual reason to reach for a z-score or a robust scaler instead.