The data set we'll be using in the midterm assignment, ClaimsData.csv$,$ is structured to represent a sample of patients in the Medicare program, which provides health insurance to Americans aged $65$ and older, as well as some younger people with certain medical conditions. The observations represent a $1\%$ random sample of Medicare beneficiaries, limited to those still alive at the end of $2008.$ Our independent variables are from $2008,$ and we will be predicting cost in $2009.$ Our independent variables are the patient's age in years at the end of $2008,$ and then several binary variables indicating whether or not the patient had diagnosis codes for a particular disease or related disorder in $2008$: alzheimers, arthritis, cancer, chronic obstructive pulmonary disease, or copd, depression, diabetes, heart.failure, ischemic heart disease, or ihd, kidney disease, osteoporosis, and stroke. Each of these variables will take value $1$ if the patient had a diagnosis code for the particular disease and value $0$ otherwise. Reimbursement$2008$ is the total amount of Medicare reimbursements for this patient in $2008.$ And reimbursement$2009$ is the total value of all Medicare reimbursements for the patient in $2009.$ Bucket$2008$ is the cost bucket the patient fell into in $2008,$ and bucket$2009$ is the cost bucket the patient fell into in $2009.$ These cost buckets are defined using the thresholds determined by data supplier. So the first cost bucket contains patients with costs less than $$3,000,$ the second cost bucket contains patients with costs between $$3,000$ and $$8,000,$ the third cost bucket contains patients with costs between $$8,000$ and $$19,000,$ and the fourth cost bucket contains patients with costs between $$19,000$ and $$55,000,$ and fifth cost bucket contains patients greater than $$55,000.$ Q$1$: Calculate the patient number percentages of each bucket by creating a table of the variable bucket$2009$ and divide by the number of rows in Claims. Our goal will be to predict the cost bucket the patient fell into in $2009$ using a CART model. But before we build our model, we need to split our data into a training set $($ClaimsTrain$)$ and a testing set $($ClaimsTest$).$ Therefore, load the package caTools, and then set our random seed to $88$ so that we all get the same split. And set SplitRatio to be $0\mathrm{.}6.$ Q$2$: What is the average age of patients in the training set, ClaimsTrain? Q$3$: What proportion of people in the training set $($ClaimsTrain$)$ had at least one diagnosis code for diabetes? The baseline method would predict that the cost bucket for a patient in $2009$ will be the same as it was in $2008.$ It can be calculated by creating a classification matrix to compute the accuracy for the baseline method on the test set. And it is $0\mathrm{.}68.$ Our goal will be to create a CART model that has an accuracy higher than $68\%$ Build your CART model and name it ClaimsTree. Independent variables you should use: age, arthritis, alzheimers, cancer, copd, depression, diabetes, heart.failure, ihd, kidney, osteoporosis, stroke, bucket$2008$ and reimbursement$2008.$ Q$4.$ Construct the model in ClaimsTrain data $,$ set cp$=0\mathrm{.}00005($obtained by cross validation$)$ and predict the bucket$2009$ in ClaimTest and compute the accuracy. can you please solve these questions using R programming language $($Rstudio$)$