Mutate lab
Create the column the analysis actually needs
Revenue alone hides acquisition spend. Add net revenue before judging the campaign.
Puzzle target
Turn on Net revenue and chart that derived metric.
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
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.
Flash cards
Review the key moves
What is the main idea behind Data Science Practice: Clean and Mutate Columns?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
Which statement best captures the main point of this lesson?
Put the learning moves in the order that makes the concept easiest to apply.
Before charting or modeling a dataset, which move should come first?
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. |
Practice Task
- 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.