A$-3.[10$ 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.

A$-4.[10$ marks: $2\mathrm{.}5$each : Using the same dataset split in A$-3.$a

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ISE$-291$: Homework $04$

a$.$ Build a Random forest classifier for predicting the class label with $4$ 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.

d$.$ Repeat A$-4($a$-$c$)$ using a Random forest with $8$ trees instead of $4.$ A$-5.[10$ marks$]$: Calculate the Information Gain $($IG$)$ for the class variable "Drug" given the feature

selected $"$BP$"$ as a root node.

A$-6.[10$ marks$]$: From the decision tree built in A$-3,$ write three classification rules using the

normalized values first then return it to the original values.

A$-7.[10$ marks$]$: Write an association rule for $"$ BP $->$ Cholestrol", which rule has the highest

accuracy? Write the corresponding support and accuracy.

A$-8.[10$ marks$]$: Repeat parts b$,$ c$,$ and d in A$-3$ using the Na$$ve Bayes GaussianNB classifier.

A$-9.$ 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 datat set? Problem A $[100$ Marks$]$: Solve all the questions using Python. Use Pandas, Seaborn, Sklearn, etc.,

libraries for all the analysis. Consider the data given in Excel file HW$4\_$DataA. Consider the following

data description:

Table $1.$ Data description

Do the following tasks $($in exact sequence$)$ using the $"$HW$4\_$DataA" data:

A$-1.[5$ marks$]$: Read and display the data given in HW$4\_$DataA. Describe both the numeric and

categorical attributes. Refer to Table $1$ for the data description.

A$-2.[10$ 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.

b$.$ Perform ordinal $($label$)$ encoding for ordinal attributes $($BP$,$ and Cholestrol$).$ Use dictionary

for the ordinal encoding.

c$.$ Perform one hot encoding for the categorical attribute $($Sex$)$

d$.$ Perorfm label encoding for the class $($drug$).$