Exercise

# PCA - rotation

**Principal Component Analysis** allows you to reduce the number of dimensions in a dataset, which speeds up calculation time without significant loss of informational value.

You may expect questions on PCA during the interview if your future role involves handling vast amounts of data.

Recall that in PCA the variables are transformed into principal components. The first principal component has the largest possible variance.

You will perform PCA using the `cats`

dataset that you have already encountered in the previous exercises.

In this exercise, use `prcomp()`

to perform the principal component analysis. The returned object can be used to **predict** the rotated variables.

Instructions 1/2

**undefined XP**

- Draw a plot of
`Bwt`

and`Hwt`

from the`cats`

dataset. - Perform PCA on
`Bwt`

and`Hwt`

from the`cats`

dataset. - Derive the summary of the PCA.