31RanRegression

Number of orders

I encoded store type, location type, holiday and discount as numbers and trained LightGBM to predict daily orders across 188,340 store-days.

What it printed in 2022

Numbers read from the saved notebook outputs. Cell numbers count every cell in the notebook, Markdown included, starting at 1.
Output	Value	Cell #
Test rows predicted (LightGBM)	37,668	21

Cell # counts every cell from the top of the notebook, Markdown included, starting at 1.

Predictions were printed for 37,668 test rows, but never compared with the real orders.

A supplement retailer's store-day table from the tutorial author's data repository.

Score it, and split by date so the model never sees the future.
Add the store ID and day of week. Four coarse categories cannot tell two stores of the same type apart.