Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a popular unsupervised learning technique used for dimensionality reduction and feature extraction. PCA transforms a high-dimensional dataset into a lower-dimensional space while retaining the maximum amount of variance in the data.

Principal Component Analysis (PCA) works by finding a set of orthogonal vectors, called principal components. It captures the maximum amount of variance in the data. The first principal component is the direction in which the data varies the most. Each subsequent component is chosen to be orthogonal to the previous ones. It captures as much remaining variance as possible. To get the full article please visit : https://myperfectthings.com/principal-component-analysis-pca/