Unsupervised Learning: Clustering and Dimensionality Reduction

Apply K-Means clustering and interpret the resulting clusters.

Use the elbow method and silhouette score to select the optimal number of clusters.

Apply hierarchical clustering and interpret the resulting dendrogram.

Perform dimensionality reduction using Principal Component Analysis (PCA).

Explain the concept of explained variance in the context of PCA.

Visualize high-dimensional data in two dimensions using PCA.