Create a script to compute the number of feature dimensions $N$ needed to represent at least $99\mathrm{.}9\%$ of the variance in the feature set of the humanactivity dataset using the 'pca' function.

The steps are:

Compute eigvals using the 'pca' function

Define vector cumulative$\_$percent$\_$variance$\_$permode, which is a vector the same size as eigvals that contains $100$ times $($to convert fraction to percentage$)$ the cumulative sum of the normalized eigenvalues

Define $N$ as the number of eigenvectors needed to capture at least $99\mathrm{.}9\%$ of the variation in our dataset $D.$