Plan an unsupervised clustering segmentation
Use to set up a clustering analysis: feature prep, algorithm choice, and how to validate clusters.
Act as a data scientist running customer or item segmentation.
Entities to cluster: {{entities}}
Available features: {{features}}
Goal of the segmentation: {{goal}}
Number of clusters expected (if any prior): {{cluster_prior}}
Task:
1. Recommend feature preprocessing (scaling, encoding, dimensionality reduction) and why each step matters for clustering.
2. Recommend an algorithm (k-means, hierarchical, DBSCAN, GMM) given the feature types and goal, with the trade-offs.
3. Describe how to choose k (elbow, silhouette, gap statistic) or density params.
4. Explain how to profile and name the resulting clusters so they are actionable.
5. Give the Python skeleton end to end, assuming a dataframe df.
Warn me about features that would dominate distance if not scaled.Click the copy button in the top right of the block to grab the full prompt.
Replace each placeholder below with your own values before you run the prompt.
- {{entities}}
- {{features}}
- {{goal}}
- {{cluster_prior}}
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