THE RESULT
What the data shows
The largest computed metric is 213 for 2.0; the smallest is 80 for 0.0. Metric: customers (Customers).
Exploratory silhouette=0.259. Three clusters were prespecified; no claim of natural or stable business segments.
| cluster | fresh | milk | grocery | frozen | detergents paper | delicassen | customers |
|---|---|---|---|---|---|---|---|
| 0 | 1,492.50 | 6,300.50 | 10,502.50 | 376 | 4,217.50 | 434.5 | 80 |
| 1 | 12,126 | 7,184 | 9,965 | 2,005 | 3,378 | 2,005 | 147 |
| 2 | 9,612 | 1,601 | 2,155 | 2,121 | 274 | 686 | 213 |
THE METHOD
From source to answer
Three-cluster K-means on standardized log spending, with silhouette and median profiles.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Review cluster profiles with a commercial owner before assigning business labels or actions.
Where the conclusion stops
Annual customer spending uses source monetary units, not an assumed currency. There are no margins, transactions or dates. Customer segments are descriptive and do not establish promotion response.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.