Principal Component Analysis 편집하기 (부분)

==Steps in PCA==
#'''Standardize the Data:''' Center the data by subtracting the mean of each feature and scale it (if necessary).
#'''Compute the Covariance Matrix:''' Calculate the covariance matrix of the dataset to understand relationships between features.
#'''Calculate Eigenvectors and Eigenvalues:''' Find the eigenvectors and eigenvalues of the covariance matrix to determine the principal components and their variance contribution.
#'''Select Principal Components:''' Retain the top k principal components that explain the majority of the variance.
#'''Transform the Data:''' Project the original data onto the new feature space defined by the selected principal components.