Please use R Programming and R Studio for this question.

Link to the file of the data for this question: https://drive.google.com/file/d/18kGNrHUfgcVv2hMKqL5E05L40xCl6e1M/view?usp=sharing

(Use the data file to answer the questions shown in the attached images):

Question 1: For this problem, Ivou will load and perform some cleaning steps on a dataset in the provided EiankData.csv, which is data about loan approvals from a bank in Japan {it has been modied from the original for our purposes in class, so use the provided version]. Specificallv, vou will use visualization to examine the variables and normalization, binning and smoothing to change them in particular wavs. a. 'v'isualize the distributions of the variables in this data. You can choose bar graphs, histograms and densitv plots. Make appropriate choices given each type of variables and be careful when selecting parameters like the number of bins for the histograms. Note there are some numerical variables and some categorical ones. The ones labeled as a \"bool' are Boolean variables, meaningthev are onlv true or false and are thus a special tvpe of categorical. Checking all the distributions with visualization and summarvstatistics is a tvpical step when beginning to work with new data. b. How applv normalization to some of these numerical distributions. Specificallv, choose to applv mm one, min-max to another, and decimal scaling to a third. Explain vour choices of which normalization applies to which variable in terms of what the variable means, what distribution it starts with, and how the normalization will affect it. c. \"visualize the new distributions for the variables that have been normalized. wh at has changed from the previous visualization in step a? d. For contl, create a new variable called contl_bins that is a binned version of that variable. This contl_bins will have a new set of values like low, medium, high. Low ranges from -|nf to 25, Medium ranges from 25 to 4-D, and High ranges from 4D to Inf. Show this binned version contl_bins along with the other data from the dataset. Assign numerical values to the bins using the bin-mean and show the result. e. Building on {cl}, use contl_bins to create a smoothed version of contl and displayI the new distribution. How is this new distribution different than the previous distribution for contl? 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