Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical technique used to simplify complex data sets. Imagine you have a large collection of features describing objects—like their size, color, and weight. PCA identifies the most important features, or "principal components," that capture the most variability in the data. By focusing on these key components, PCA reduces the number of dimensions while retaining essential information, making it easier to visualize and analyze the data. This technique is commonly used in data analysis, machine learning, and image processing to reveal patterns and relationships within the data.