Story points | 8 |
Tags | kmeans sklearn clustering |
Hard Prerequisites | |
IMPORTANT: Please review these prerequisites, they include important information that will help you with this content. | |
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Read through the K-Means Tutorials at TOPIC: K-Means Clustering before starting this project.
Data: Whisky Tasting Profiles
Use K-Means clustering to cluster whisky distilleries by their tasting profile. Use the elbow or silhouette method to find the optimal number of clusters.
To see how successful clustering was, report relevant metrics (e.g. silhouette, adjusted rand index, etc.) and create a plot showing the different distilleries, their classes according to the k-Means clustering, and the distance between points. You can use sklearn.manifold
to get Euclidean distances between points.
Describe the main differences between the cluster - what are the factors that differ between classes?