10th Nov 2023

Principal Component Analysis (PCA) is a dimensionality reduction technique used in statistics and machine learning to streamline complex datasets. It achieves this by transforming the original features into a set of uncorrelated variables called principal components. These components capture the directions in the data with the highest variance, allowing for a more compact representation of the dataset. The first principal component accounts for the maximum variance, and subsequent components capture orthogonal directions with decreasing variance. By selecting a subset of these components, PCA enables a reduction in dimensionality while retaining the essential information present in the original dataset.

PCA is widely applied in various domains for its ability to simplify high-dimensional data and alleviate issues related to multicollinearity and noise. It is instrumental in tasks such as feature extraction, image processing, and exploratory data analysis, providing analysts and researchers with a powerful tool to gain insights into complex datasets and improve the efficiency of subsequent analyses or machine learning models.