What is the role of Principal Component Analysis (PCA) in handling Multicollinearity?

PCA assigns weights to variables based on their importance.
PCA creates new uncorrelated variables from the original set of correlated variables.
PCA eliminates variables with low variance.
PCA increases the dimensionality of the data set.

PCA creates a new set of uncorrelated variables (principal components) from the original set of correlated variables. These principal components are linear combinations of the original variables, and they are orthogonal to each other, hence eliminating multicollinearity.

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