Objective: The primary objective of this project is to apply unsupervised learning techniques, specifically K-Means clustering and Principal Component Analysis (PCA), to the California Housing dataset. The goal is to:

Identify natural groupings (clusters) within the housing data. Reduce the dimensionality of the dataset for visualization purposes. Detect and highlight anomalous data points based on reconstruction error, indicating samples that deviate significantly from the learned data patterns.