Principal Component Analysis (PCA) is a valuable statistical analysis technique widely utilized for dimensionality reduction and feature extraction. The primary objective of PCA is to transform a set of correlated variables into a new set of uncorrelated variables, called principal components, by capturing the most significant variance in the data. The process involves standardizing the data, calculating the covariance matrix, and performing eigendecomposition to obtain eigenvectors and eigenvalues. The eigenvectors with the highest eigenvalues, representing directions of maximum variance, are selected as principal components. PCA finds application in various domains, such as dimensionality reduction in high-dimensional datasets, visualization to uncover patterns, noise reduction by focusing on significant variations, and feature extraction to enhance the performance of machine learning models. By summarizing complex data structures, PCA facilitates more efficient and insightful statistical analyses.