Question: Data Mining Q4. You are given a n-dimension dataset (n - 6) and you perform Principal Component Analysis (PCA) on it. The result of the

Data Mining

Data Mining Q4. You are given a n-dimension dataset (n - 6)

Q4. You are given a n-dimension dataset (n - 6) and you perform Principal Component Analysis (PCA) on it. The result of the PCA and the corresponding Scree plot are below: Importance of components Std. Dev: PC3 PC4 PC1PC2 1.7312 1.43770.8069 0.41003 0.29783 0.16781 PCS PC6 Proportion of variance: 0.4995 0.3445 0.1085 0.02802 0.01478 0.00469 2 3 6 Based on the above information, answer the questions below: (a) Which principal component (PC) explains the most variance and which explains the least variance? (b) How many PCs would you use above for subsequent modeling? (c) For the number of PCs you chose in (b), how much total percentage of variance is explained by these PCs

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