Clustering Visualization
Presenting segmentation results
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I performed K-Means clustering with [K] clusters on a dataset of [SIZE] observations with [NUMBER_OF_FEATURES] features. Generate a comprehensive visualization plan: dimensionality reduction to visualize the clusters, profile of each cluster with key features, and how to name and interpret each segment.
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Indiquez au modèle une méthode de réduction dimensionnelle adaptée : PCA si vous cherchez la variance globale, UMAP/t-SNE pour des structures non linéaires. Pour les profils, demandez une comparaison des moyennes par cluster aux moyennes globales, avec un seuil d’écart, afin de ne retenir que les features discriminantes. Précisez aussi le contexte métier : les noms de segments seront bien plus actionnables.
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