Filter lab
Filter the rows before reading the signal
Find the high-satisfaction West market before you compare revenue.
0/3 checks
Puzzle target
Set Region to West and Satisfaction to 80 or higher.
○Region filter is West
○Satisfaction cutoff keeps only strong rows
○Vancouver is the remaining row
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 | Filter a small campaign dataset until the signal is isolated. |
Practice Task
- Use row filters to keep the high-satisfaction West market, then read the remaining table and chart.
- 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.