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Explain the concept of principal component analysis?
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Answer / Swatantra Kumar Maurya
Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. These new variables are an uncorrelated orthogonal basis set, which can be thought of as the directions in which the data varies most.nnIn simpler terms, PCA helps to reduce the dimensionality of the dataset by finding the linear combination of original variables that explain the maximum variance. This is useful when dealing with high-dimensional datasets where visualization and interpretation become challenging.
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