Dimensionality Reduction on Face using PCA

Machine Learning has a wide variety of dimensionality reduction techniques. It is one of the most important aspects in the Data Science field. As a result, in this article, I will present one of the most significant dimensionality reduction techniques used today, known as Principal Component Analysis (PCA). But first, we need to understand what Dimensionality Reduction is and why it is so crucial. Dimensionality reduction, also known as dimension reduction, is the transformation of data from a high-dimensional space to a low-dimensional space in such a way that the low-dimensional representation retains some meaningful properties of the original data, preferably close to its underlying dimension.