H) Principal Component Analysis

Tue, 05 May 2026 05:23:38 +0000

# Principal Component Analysis (PCA): Unlocking Insights Through Dimensionality Reduction Principal Component Analysis (PCA) is one of the most widely used techniques in data science, machine learning, and statistical analysis for reducing the dimensionality of large datasets. Whether you're preparing data for visualization, improving model performance, or uncovering hidden patterns, PCA serves as a powerful tool that simplifies complex data without sacrificing essential information. ## What is Principal Component Analysis (PCA)? PCA is a dimensionality reduction method that transforms a high-dimensional dataset into a lower-dimensional space. It achieves this by identifying the principal components—orthogonal (non-correlated) axes—that capture the maximum variance in the data. These components are linear combinations of the original variables, ordered by the amount of information (variance) they retain.