How PCA reduces dimensions, why standardizing features first matters, and how to read explained variance, with worked Python examples.
Dimensionality Reduction with Principal Component Analysis (PCA) Read More »
How medoids resist outliers, how K-medoids (PAM) differs from K-means, and when to choose it, with worked Python examples.
K-Medoids Clustering: A More Robust Alternative Read More »
How K-means groups data, why feature scaling matters, and how to find customer segments in Python, with a hands-on worked example.
K-means Clustering: A Practical Guide to Understanding Customer Patterns Read More »
How UMAP reduces high-dimensional data to a 2D map, when it beats PCA, and how to tune its key parameters, with interactive visuals and Python.
Dimensionality reduction with Uniform Manifold Approximation and Projection (UMAP) Read More »
