Data Mining

tep 3: perform Principal component analysis (PCA) to generate a number of Principal Components (PCs) capturing 85% of total data variance. a) Plot percentage of variances of each Principal Components(PC) in a decreasing order (5 points) b) How many components do you need to capture >85% total data variance? (5 points) c) Plot the generated (new) top P PC variables you selected (5 points) d) Compute the covarion matrix of the P PC variables (this should be P-by-P matrix), and compute the total variance of PCs (sum of diagonal elements of covariance matrix ), compare this value with the total variance of data in Step2_b, what \% variance kept in PCs (5 points), e) compute correlation (Pearson's correlation) between new variable PC1 and new variable PC2 (5 points), f) Plot the PC coefficients (or projection direction) of N components you selected (5 points)