THE RESULT
What the data shows
The largest computed metric is 6.113 for 3.0; the smallest is 5.399 for 0.0. Metric: mean_quality (Sensory score).
Exploratory silhouette=0.229; K=4 was fixed before fitting. Clusters may mainly reflect wine type.
| cluster | samples | mean quality | higher rated share | red share |
|---|---|---|---|---|
| 0 | 966 | 5.399 | 0.05694 | 0.9193 |
| 1 | 2,138 | 5.632 | 0.1034 | 0.008419 |
| 2 | 707 | 5.836 | 0.2207 | 0.9349 |
| 3 | 2,686 | 6.113 | 0.3146 | 0.01191 |
THE METHOD
From source to answer
Four K-means groups on standardized log chemical measurements; sensory labels excluded during fitting.
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
Inspect chemical profiles without assigning quality meanings to cluster numbers.
Where the conclusion stops
Sensory scores are ordinal and concentrated in the middle. Producer and batch IDs are unavailable. Associations are not recipes for changing quality or evidence of market price.
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.