Reading cancelled before trusting it in flight-delays

Profiling cancelled on flight-delays before anyone builds anything on top of it.

8,000 rows, no nulls, 2 distinct values. Whether the flight was cancelled.

It is true in 2.1% of rows — 167 of 8,000.

That imbalance is the single most important fact about the column. It sets the baseline any model has to beat, it decides whether a per-segment breakdown will have enough rows in the minority class to say anything, and it determines how wide the confidence interval on any rate computed from it will be.

select
  count(*)                    as rows,
  count(cancelled)            as present,
  count(*) - count(cancelled) as nulls,
  count(distinct cancelled)   as distinct_values
from flight_delays;

A base rate this low is the reason accuracy is the wrong metric on anything predicting this column: always predicting false scores 97.9% and learns nothing.

Where this bites: slicing by a dimension with 16 levels leaves roughly 10 true rows per slice on average. That is thin enough that the noisiest segment will look like the most extreme one, every time, and someone will read the ranking as a finding.

The grain is one row per scheduled flight, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.