# Import necessary libraries

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

from sklearn.tree import DecisionTreeClassifier, plot$\_$tree

from sklearn.metrics import classification$\_$report

import pandas as pd

import matplotlib.pyplot as plt

# Sample data $-$ replace this with your actual data

data $=\{$

'Age': $[24,38,34,44,23,56,45,60,71,18],$

'Gender': $[\text{'}$Woman$\text{'},$ 'Man', 'Man', 'Woman', 'Woman', 'Woman', 'Man', 'Woman', 'Man', 'Man'$],$

'Health Status': $[\text{'}$Good$\text{'},$ 'Sick', 'Good', 'Sick', 'Sick', 'Good', 'Good', 'Sick', 'Good', 'Good'$],$

'Employment Status': $[\text{'}$Manager$\text{'},$ 'Worker', 'Unemployed', 'Worker', 'Manager', 'Unemployed', 'Worker', 'Official', 'Official', 'Manager'$],$

'Salary': $[1000,500,0,150,100,900,0,850,700,420],$

'Loan Granted': $[\text{'}$Yes$\text{'},$ 'Yes', $\text{'}$No$\text{'},$ 'Yes', $\text{'}$No$\text{'},$ 'Yes', $\text{'}$No$\text{'},$ 'Yes', $\text{'}$No$\text{'},$ 'Yes'$]$

$\}$

# Convert the data to a DataFrame

df $=$ pd$.$DataFrame$($data$)$

# Convert categorical variables to numerical using one$-$hot encoding

df$\_$encoded $=$ pd$.$get$\_$dummies$($df$,$ columns$=[\text{'}$Gender$\text{'},$ 'Health Status', 'Employment Status'$])$

# Separate the features $($X$)$ and the target variable $($y$)$

X $=$ df$\_$encoded.drop$(\text{'}$Loan Granted', axis$=1)$

y $=$ df$\_$encoded$[\text{'}$Loan Granted'$]$

# Split the dataset into training $(80\%)$ and testing $(20\%)$ sets

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

# Initialize the Decision Tree Classifier

clf $=$ DecisionTreeClassifier$()$

# Train the model on the training set

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

# Predict the target variable for the test set

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

# Evaluate the model's performance

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

print$("$Classification Report:

$",$ report$)$

# Visualize the decision tree

plt$.$figure$($figsize$=(12,8))$

plot$\_$tree$($clf$,$ filled$=$True, feature$\_$names$=$X$.$columns, class$\_$names$=[\text{'}$No$\text{'},$ 'Yes'$])$

plt$.$title$("$Decision Tree Visualization"$)$

plt$.$show$()$