Please use R Programming for this question and take screenshots of code once finished:

Question 2: We will use SVM in this problem, showing how it often gets used even when the data are not suitable, by first engineering the numerical features we need. There is a Star Wars dataset in the dplgr library. Load that library and you will be able to see it {headlstarwarsl}. a. There are some variables we will not use, so first remove films, vehicles, starshjpg and name. Also remove rows with missing values b. Several variables are categorical. We will use dummy variables to make it possible for SVM to use these. Show the resulting head of the dummy variables including the target column gender. c. Use SUM to predict gender and report the accuracy. First, create the dataset for 66% training and 34% testing and a seed of 94 for the random partitioning. d: Given that we have so many variables, it makes sense to consider using PCA. Run PCA on the data and determine an appropriate number of components to use from the graph. Create a reduced version of the data with that number of principle components by first finding and removing near zero variance predictors using the following code: nzv c nearZeroVar{numeric train] W W filtered