Given the admission dataset, implement the following tasks in Python.

$($a$)$ Consider admit and notdmit samples only. Assume data follow a joint normal distribution. Build a Bayes classifier and evaluate its performance using a $5-$fold cross validation approach. You will need to randomly split the dataset into $5$ subsets. $4$ subsets are used for training your classifier and the remaining $1$ subset is used for testing purpose. Repeat training and testing for all combinations of subsets. Construct confusion matrices and then calculate accuracies for both training and

testing datasets. You can set a cut$-$off $=0\mathrm{.}5$ if needed.

$($b$)$ Consider admit and notdmit samples only. Build a KNN classifier and evaluate its

performance using a $5-$fold cross validation approach. You will need to randomly split the dataset into $5$ subsets. $4$ subsets are used for training your classifier and the remaining $1$ subset is used for testing purpose. Repeat training and testing for all combinations of subsets. Construct confusion matrices and then calculate accuracies for both training and testing datasets. Specify k and distance measurement on your own.

$($c$)$ Standardize $($or rescale$)$ both datasets and redo parts $($a$)$ and $($b$).$