Principle component analysis converts correlated variables into uncorrelated variables using orthogonal transformation in a statistical procedure. Principle component analysis is used to study the interrelation between a set of variables. This algorithm is used to consider a large dataset of interconnected variables and chooses the set which best suits a model. This type of concentration of variables is known as dimensionality reduction. This method helps to reduce the complication of sets of variables. This analysis is also known as general factor analysis.
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