Create the UML Class Diagram for the code as it is given here. Download the code here. $($click on here to download code$).$ Add a method in class, use your name as methodname $($firstNamelastName$).$ Use Visual Paradigm and export the resulting diagram as an image which you can insert in the response box. If you cannot able to insert the image use upload $/$AddFile option in Quiz.# Import necessary libraries

import seaborn as sns

import pandas as pd

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import classification$\_$report

# Load the penguin dataset

df $=$ sns$.$load$\_$dataset$(\text{'}$penguins$\text{'})$

# Display the first few rows of the dataset

print$($df$.$head$())$

# Data Preprocessing

# Drop rows with missing values

df$.$dropna$($inplace$=$True$)$

# Encode categorical features

df$[\text{'}$sex$\text{'}]=$ df$[\text{'}$sex$\text{'}].$map$(\{\text{'}$Male$\text{'}$: $0,$ 'Female': $1\})$

df $=$ pd$.$get$\_$dummies$($df$,$ columns$=[\text{'}$species$\text{'},$ 'island'$],$ drop$\_$first$=$True$)$

# Define features and target

X $=$ df$.$drop$(\text{'}$sex$\text{'},$ axis$=1)$

y $=$ df$[\text{'}$sex$\text{'}]$

# Split the data into training and testing sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

# Train a Decision Tree Classifier

clf $=$ DecisionTreeClassifier$($random$\_$state$=42)$

clf$.$fit$($X$\_$train, y$\_$train$)$

# Predict on the test set

y$\_$pred $=$ clf$.$predict$($X$\_$test$)$

# Evaluate the model

print$($classification$\_$report$($y$\_$test, y$\_$pred$))$