Bugl — code interview practice that feels like a game

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Mutate lab

Create the column the analysis actually needs

Revenue alone hides acquisition spend. Add net revenue before judging the campaign.

1/3 checks

Puzzle target

Turn on Net revenue and chart that derived metric.

○Net revenue column is visible

○Chart metric uses net revenue

✓Rows are still available for comparison

Working dataset

City	Region	Visits	Signups	Revenue	Satisfaction
Vancouver	West	1,200	156	$18,400	86
Calgary	West	860	95	$9,900	74
Toronto	Central	2,100	252	$32,600	82
Montreal	East	1,580	181	$21,200	79
Halifax	East	640	83	$8,700	88

Revenue visual

Vancouver

$18,400

Calgary

$9,900

Toronto

$32,600

Montreal

$21,200

Halifax

$8,700

Tiny model

Feature

Visits

Train

60%

Predicted revenue

$21,793

This is intentionally small: change one feature, keep a holdout split, and explain what changed before trusting the model.

Work directly with the dataset lab below. The controls change the rows, derived columns, visual, and tiny model summary in place.

Practice surface	What you manipulate
Dataset	Campaign rows with visits, signups, revenue, satisfaction, and promo cost
Transform	Filters, derived net revenue, metric choice, and sorting
Model	One-feature prediction with a train and holdout split
Goal	Create a derived net-revenue column before comparing performance.

Add the net revenue column, chart it, and compare it against raw revenue.
Watch the checklist in the lab update as the dataset state changes.
Use the table, visual, and model card together before deciding what the data says.