An unsupervised learning algorithm technique called Principal Component Analysis (PCA) is used to look at how a set of variables relate to one another. PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. PCA is a dimensionality reduction technique that is reducing the number of variables in face recognition.
Moreover, PCA is used to create a new collection of variables that are smaller than the original set of variables, preserve the majority of the sample’s information, and are helpful for data regression and classification in order to minimize the dimensionality of a data set. Principal Component Analysis can be used for a variety of purposes such as data visualization, feature selection, and data compression.
Data compression: High-dimensional datasets can be made easier to store and interpret by using PCA to reduce their dimensionality.
Feature extraction: Using PCA, one can determine which elements in a dataset are most crucial for developing predictive models.
Visualization: High-dimensional data can be seen in two or three dimensions using PCA, which facilitates understanding and interpretation.