(iii) Extract only the data for the numeric features in mydata. along with APT; and store them as adata frame ftibble .Then , perform PEA. usingpmomp (,1 in R..but only on the nifmeric features. Outline why you believe the data should or should not be scaled , i.e. standardised . when performing PEA . Outline the individual and cumulative proportions of variance [3 decimal places} explained by each ofthe first 4 components. Outline hon.r many principal components are adequate to explain at least 50% of the variability in your data . Outline the coefficients {or loadings} to 3 decimal places for PCI, PCZ and PCB. and describe which features {based on the loadings )are the key drivers for each of these three PEs. [iv] Create a biplot for 1] PCI vs PEE, 2}PC1 vs PCS and 3) FEE vs PCEJ to help visualise the results of your PEA in the rst three dimensions .Colour code the points with the variable APT.'Write a paragraph to explain what each of your biplots are showing . That is. comment on the PCA plot.the loading plot individually .and then both plots combined [see Slides 23 -29 of Module 3 notes ') and outline and justify which (if any) of the features can help to distinguish APT activity . (v) Based on the results from parts (iii) to (v), describe which dimension {choose one) can assist with the classication of malwares {H int: project all the points in the PCA plot to Fill axis and see whether there is good separation between the points for known and unknown APT actors . Then project to PC2 aids and see ifthene is separation between the malware and non - malware ,and whether it is better than the projection to PCI ,then likewise with PCS}. the key features in this dimension that can drive this process {Hint '. based on your decision above, examine the loadings from part{iii) ofyour chosen PC and choose those whose absolute loading (Le. disregard the sign]: is greater than 13.3 )