This is a milestone for the Coursera Data Science Capstone project.

This study applies unsupervised clustering methods to specialty arabica coffee flavor profiles, using expert-rated sensory attributes (Aroma, Flavor, Aftertaste, Acidity, Body, and Balance) sourced from the Coffee Quality Institute. Both k-means and k-medoids (PAM) algorithms were tested, with the optimal number of clusters evaluated via silhouette width, the elbow method, and the gap statistic. Despite preprocessing steps to handle outliers and a high Hopkins statistic when sweetness was included, removing the near-constant sweetness variable reduced clusterability sharply (Hopkins ≈ 0.54). Neither algorithm produced well-separated, meaningful clusters. The findings suggest that high-quality arabica coffees are remarkably homogeneous in their sensory profiles, making data-driven flavor grouping unreliable on this dataset.

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