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' 2 4 , 3 8 , 3 4 , 4 4 , 2 3 , 5 6 , 4 5 , 6 0 , 7 1 , 1 8 , 'Gender' ' Woman ' , 'Man', 'Man', 'Woman', 'Woman', 'Woman', 'Man', 'Woman', 'Man', 'Man' , 'Health Status' ' Good ' , 'Sick', 'Good', 'Sick', 'Sick', 'Good', 'Good', 'Sick', 'Good', 'Good' , 'Employment Status' ' Manager ' , 'Worker', 'Unemployed', 'Worker', 'Manager', 'Unemployed', 'Worker', 'Official', 'Official', 'Manager' , 'Salary' 1 0 0 0 , 5 0 0 , 0 , 1 5 0 , 1 0 0 , 9 0 0 , 0 , 8 5 0 , 7 0 0 , 4 2 0 , 'Loan Granted' ' Yes ' , 'Yes', ' No ' , 'Yes', ' No ' , 'Yes', ' No ' , 'Yes', ' No ' , '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 ' Gender ' , 'Health Status', 'Employment Status' ) Separate the features ( X ) and the target variable ( y ) X df encoded drop ( ' Loan Granted', axis 1 ) y df encoded ' Loan Granted' Split the dataset into training ( 8 0 ) and testing ( 2 0 ) sets X train, X test, y train, y test train test split ( X , y , test size 0 2 , random state 4 2 ) 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 ( 1 2 , 8 ) ) plot tree ( clf , filled True, feature names X columns, class names ' No ' , 'Yes' ) plt title ( Decision Tree Visualization ) plt show ( ) Programda bu ekilde bir hata al yorum z m i in acil yard ma ihtiyac m var

The Answer is in the image, click to view ...

Question: # Import necessary libraries from sklearn.model _ selection import train _ test _ split from sklearn.tree import DecisionTreeClassifier, plot _ tree from sklearn.metrics import classification

# 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':

['

Woman

',

'Man', 'Man', 'Woman', 'Woman', 'Woman', 'Man', 'Woman', 'Man', 'Man'

],

'Health Status':

['

Good

',

'Sick', 'Good', 'Sick', 'Sick', 'Good', 'Good', 'Sick', 'Good', 'Good'

],

'Employment Status':

['

Manager

',

'Worker', 'Unemployed', 'Worker', 'Manager', 'Unemployed', 'Worker', 'Official', 'Official', 'Manager'

],

'Salary':

[1000, 500, 0, 150, 100, 900, 0, 850, 700, 420],

'Loan Granted':

['

Yes

',

'Yes',

'

',

'Yes',

'

',

'Yes',

'

',

'Yes',

'

',

'Yes'

]

}

# Convert the data to a DataFrame

=

.

DataFrame

(

data

)

# Convert categorical variables to numerical using one

-

hot encoding

_

encoded

=

.

get

_

dummies

(

,

columns

= ['

Gender

',

'Health Status', 'Employment Status'

])

# Separate the features

(

)

and the target variable

(

)

=

_

encoded.drop

('

Loan Granted', axis

= 1)

=

_

encoded

['

Loan Granted'

]

# Split the dataset into training

(80 %)

and testing

(20 %)

sets

_

train, X

_

test, y

_

train, y

_

test

=

train

_

test

_

split

(

,

,

test

_

size

= 0.2,

random

_

state

= 42)

# Initialize the Decision Tree Classifier

clf

=

DecisionTreeClassifier

()

# Train the model on the training set

clf

.

fit

(

_

train, y

_

train

)

# Predict the target variable for the test set

_

pred

=

clf

.

predict

(

_

test

)

# Evaluate the model's performance

report

=

classification

_

report

(

_

test, y

_

pred

)

("

Classification Report:

",

report

)

# Visualize the decision tree

plt

.

figure

(

figsize

= (12, 8))

plot

_

tree

(

clf

,

filled

=

True, feature

_

names

=

.

columns, class

_

names

= ['

',

'Yes'

])

plt

.

title

("

Decision Tree Visualization"

)

plt

.

show

()

Programda bu

ekilde bir hata al

yorum

.

in acil yard

ma ihtiyac

m var.

# Import necessary libraries from sklearn.model_selection import train_test_split from sklearn.tree import

Step by Step Solution

There are 3 Steps involved in it

1 Expert Approved Answer

Step: 1 Unlock blur-text-image

Question Has Been Solved by an Expert!

Get step-by-step solutions from verified subject matter experts

Step: 2 Unlock

Step: 3 Unlock

Students Have Also Explored These Related Databases Questions!

import numpy as np #machine learning tool used for efficient array processing import pandas as pd #machine learning tool used for data sets and data frames from sklearn.model _ selection import train...

Get to know the lab corpus (i.e. dataset) Prepare the data by make them all in lowercase to ease the string comparisons needed for subsequent steps \# In these two steps, we lower the letters in each...

This question is solved I want another solution //I get errors when running the codes // I get errors when running the codes // I get errors when running the codes // # Import necessary libraries...

# Import necessary libraries from sklearn.model _ selection import train _ test _ split from sklearn.tree import DecisionTreeClassifier, plot _ tree from sklearn.metrics import classification _...

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 ) ....

Supervised Learning Algorithms ( SVM ) , YOU NEED TO WRITE A PYTHON CODE PLEASE READ THE INSTRUCTIONS CAREFULLY, AND PLEASE DO NOT SUBMIT AN ANSWER IF IT'S INCOMPLETE . FIRST YOU NEED TO IMPORT THESE...

Supervised Learning Algorithms ( SVM ) , YOU NEED TO WRITE A CODE PLEASE READ THE INSTRUCTIONS CAREFULLY, AND PLEASE DO NOT SUBMIT AN ANSWER IF IT'S INCOMPLETE . FIRST YOU NEED TO IMPORT THESE...

Fixed.acidity * * Sum of the organic acids that are present in wine, mainly tartaric, malic, citric and succinic acids. These acids contribute to the taste, color, and stability of the wine. The...

Let's break down the tasks and solve them step by step. First, let's choose a task to focus on . Here are the three tasks: Explanation Create a neural network to recognize your face and clearly...

What is capacity planning?

What is the volume of 0.1 M AgNO3 needed to precipitate all I- and CN- in a 50.0 mL solution that is 0.0100 M in l- and 0.050O M in CN-? *

1 2 . Prepare a pension spreadsheet that summarizes relevant pension data for 2 0 6 . The following facts relate to 2 0 6 : Current service cost for accounting was $ 1 2 4 , 0 0 0 . A plan amendment...

Which of the following has been one of the impacts of the passage of the Antiterrorism and Effective Death Penalty Act There has been a proliferation of secret searches that are allowed pursuant to...

1. Have you selected an informative topic that will teach your audience members something new?

3 Are your motivations really ethical? Do you want to avoid talking about scholarship money because you think it will never materialize or because youre angry that the coach misled you?

3. Did you select an appropriate approach for your informative speech? Will your audience be able to clearly identify your approach?