Segmenting Lingerie Styles with Unsupervised Learning

Apply Principal Component Analysis (PCA) to a lingerie feature dataset to reduce dimensionality and identify the dominant underlying style dimensions (e.g., 'structure' vs. 'delicacy').

Interpret PCA loadings and scree plots to determine the number of principal components to retain and describe them in lingerie design terms.

Perform agglomerative hierarchical clustering on lingerie pieces, comparing single-linkage and complete-linkage dendrograms to identify natural style families.

Justify the selection of a specific number of clusters by cutting the dendrogram at an appropriate level to create meaningful style segments (e.g., 'minimalist', 'boudoir', 'sporty').