Do the following tasks $($in exact sequence$)$ using the $"$HW$4\_$DataA" data: B$-1./5$ marks$/$: Read and display the data given in HW$4\_$DataA. Describe both the numeric and categorical attributes. Refer to Table $8\mathrm{.}5\mathrm{.}2$ for the data description. B$-2./12\mathrm{.}5$ marks: $2\mathrm{.}5$ each$/$: Do the necessary pre$-$processing. In specific do the following: a$.$ Normalize the numeric attributes using min$-$max normalization scheme. D$.$ Perform ordinal $($label$)$ encoding for the ordinal attribute $($education $\_$level$).$ Use dictionary for the ordinal encoding. The order is as follows starting from the lowest: $\{$High School, Associate's, Bachelor's, Master's, Doctoral; C$.$ Perform one hot encoding for the categorical attributes $($gender$,$ and marital ststus$)$ d$.$ For occupation feature, encode student to $0$ and all other choices to $1($do not forget to convert the type to integer$).$ e$.$ Perorfm label encoding for the class $($loan $\_$status$).$ B$-3./10$ marks: $2\mathrm{.}5$ each$/$: a$.$ Split the dataset into training and testing sets using train$\_$test$\_$ split function with $75\%$ for training and $25\%$ for training using random state $=10.$ b$.$ Build a decision tree classifier for predicting the class label. Fit the classifier using the training dataset. Set random state to $100,$ criterion to entropy, and splitter to best. C$.$ Draw the decision tree using scikit$-$learn $($sklearn$)$ d$.$ Test the classifier on the testing data set, and print the confusion matrix and classification metrics $($Accuracy$,$ sensitivity $($Recall$),$ Precision$)$ of the decision tree classifier. B$-4.17\mathrm{.}5$ marks: $2\mathrm{.}5$ each$/$: Using the same dataset split in B$-3.$a a$.$ Build a Random Forest classifier for predicting the class label with & trees. Fit the classifier using the training set. Set criterion to entropy and random$\_$ state to $62.$ b$.$ Draw the trees using sci$-$kit learn $($sklearn$)$ C$.$ Test the classifier on the testing data set, and print the confusion matrix and classification metrics $($Accuracy$,$ sensitivity $($Recall$),$ Precision$)$ of the Random forest classifier. B$-5./10$ marks: Calculate the Information Gain $($IG$)$ for the class variable "loan$\_$status" given the feature "education $\_$level" as a root node. B$-6./10$ marks: From the decision tree built in B$-3,$ write a classification rule using the normalized values first then return it to the original values. B$-7./10$ marks$)$: Write two association rules for $"$ gender $->$ education level", which rule has the highest accuracy? Write the corresponding support and accuracy. B$-8./10$ marks$/$: Repeat parts b$,$ c$,$ and d in B$-3$ using the Na$$ve Bayes GaussianNB classifier. B$-9./5$ marks$/$: Compare the performance of the Na$$ve Bayes against the built decision tree and random forest classifiers using confusion matrix. Based on the comparison, which one is the best to use with the given data set? PLEASE solve all the parts