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📋 Unsupervised ML Decision Cards

This section contains decision cards for guiding the selection, evaluation, and application of unsupervised machine learning methods.

These resources help analysts determine the most appropriate clustering approaches and evaluate their performance based on data structure, scale, and analytical goals. They are especially useful for exploratory tasks, customer segmentation, anomaly detection, and feature engineering.

📂 Available Decision Cards

• 📋 Clustering Model Decision Card — Guidance on choosing between different clustering algorithms (e.g., K-Means, DBSCAN, hierarchical) based on data distribution and use case.
• 📋 Clustering Model Selection Decision Card — Framework for selecting the optimal clustering configuration, including distance metrics, cluster number, and validation methods.