Principal Component Analysis

Overview

Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance (information) as possible. PCA works by finding new axes, called principal components, that capture the most variation in the data. These components are linear combinations of the original features and are ranked by how much variance they explain. The first principal component captures the most variance, the second captures the next most, and so on. PCA is widely used in machine learning, data visualization, and noise reduction because it helps in understanding which variables contribute the most to the dataset, simplifies complex datasets, and improves model efficiency by removing redundant information. However, since PCA relies on variance, it is sensitive to feature scaling, so normalization is often necessary before applying it.