ISE$-291$: Homework $04$

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

$\backslash$table$[[$Field$,$Description$],[$Age$,$Patient's age.$],[$Sex$,$Patinet's gender $($Male or Female$).],[$BP$,$Blood pressure $($High$,$ Normal,Low$).],[$Cholestrol$,$Cholestrol level $($High$,$ Normal$).],[$Na$\_$to$\_$K$,$Sodium $-$ Potassium ratio.$],[$Drug$,$The drug type that the patient responded to$.]]$

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$).$

A$-3.$ 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$.$ 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 $4.$