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Showing posts from March, 2023

PCA in brief

Principal Component Analysis is the data reduction technique reducing the number of features in a few numbers while maximum number of variation is explained. This enables the proper data visualisation and interpretation of multiple dimensions of the data. Data is transformed into the new coordinate system with new axes named as principal components. Principal components are the linear transformations of the features into the new data where each data point is multiplied by some constant.  Briefly, in few steps, PCA is applied in these ways: Each data point is centred around the mean. In other words, each data point is subtracted from its respective mean. Covariance matrix is calculated between the features. Covariance matrix is the arrangement of variance and covariance of two features. For covariance of the sample, each corresponding data point subtracted from their own respective means are multiplied which is divided by the number of samples subtracted by 1. Eigen value of the covaria


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