Section 12: K-Means Clustering

So far we have done supervised learning. The remaining sections will be on unsupervised learning. Below is a quick guide on how to pick the estimator:

Source: scikit-learn

Unsupervised Learning: (1) Clustering: Using features, group together data rows into distinct clusters (2) Dimensionality Reduction: Using features, discover how to combine and reduce into fewer components

Supervised learning's performance metrics (RMSE or Accuracy) will not apply for unsupervised learning.

References:

• An Introduction to Statistical Learning (Download free pdf)
• Jose Portilla's 2021 Python for Machine Learning & Data Science Masterclass