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Create the UML Class Diagram for the code as it is given here. Download the code here. ( click on here to download code )

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('penguins')
# 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['sex']= df['sex'].map({'Male': 0, 'Female': 1})
df = pd.get_dummies(df, columns=['species', 'island'], drop_first=True)
# Define features and target
X = df.drop('sex', axis=1)
y = df['sex']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 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))

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