Summary of the Chapter#
In this chapter, we explored unsupervised learning, where models learn patterns from data without labels. We first addressed the challenge of high-dimensional data using Principal Component Analysis (PCA), which reduces the number of features while preserving the most important information by capturing directions of maximum variance. This helps simplify data, remove redundancy, and make patterns easier to understand and visualize.
PCA Workflow: Data → Covariance → Eigenvectors → Sort → Select → Project → Reduced Data
We then focused on clustering, which groups similar data points together. We studied K-Means, which forms clusters by minimizing intra-cluster distance (WCSS), along with methods like the Elbow Method and Silhouette Score to choose the number of clusters. We also covered Hierarchical Clustering, which builds a tree-like structure of clusters, and DBSCAN, a density-based method that can find arbitrarily shaped clusters and detect noise. Together, these techniques show how we can uncover meaningful structure in data even without labels.