1. Problem

Weather features look appealing because they feel intuitive, but intuition is not a good enough reason to increase pipeline complexity. The real question was whether sunshine carried enough signal to justify modeling effort.

2. Approach

I ran a quick exploratory pass that prioritized data quality, interaction effects, and sample-size visibility over fancy modeling. The aim was to decide whether the feature was worth a proper sprint.

  • Check coverage and join integrity first.
  • Use deciles and interaction grids instead of naive linear relationships.
  • Measure uplift within sites to avoid mistaking geography for signal.

3. Evidence

Mean sales by sunshine deciles
The heatmap surfaced non-linear patterns quickly, but it also made the weak sample corners obvious.
Within-site uplift by sunshine deciles
Within-site uplift was the more honest view because it stripped away the baseline differences between locations.

4. Outcome

The analysis did its job: it stopped the team from overcommitting to a pretty idea before the feature had earned the operational cost of proper integration.

5. Tech stack

  • Pandas and NumPy for fast slicing and aggregation
  • Matplotlib for decision-grade visualizations
  • Reusable EDA helpers for coverage and interaction checks

6. Useful links

7. Related reading

8. Call to action

If you need to decide whether a feature idea deserves proper modeling work, I can help design the fast analytics pass that answers the question cleanly.