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    Understanding the Mathematics Behind Principal Component Analysis(PCA)

    Principal Component Analysis (PCA) is a widely used dimensionality reduction technique in machine learning and data analysis. It aims to make a dataset change into another coordinate system in which variance of the data is maximized along new axes, referred as principal components. The dimensionality of high-dimensional data can be reduced by PCA while keeping

    Principal Component Analysis (pca): A Complete Overview

    Principal Component Analysis (PCA) is a very useful tool that is widely used for dimensionality reduction and data visualization. It helps in finding patterns and relationships in data by turning high dimensional input into a lower dimensional form with important information being retained. This is an all-inclusive guide to help you comprehend what PCA really