The file "loanacceptance.csv" contains various attributes of 500 customers based on which loan has either been granted or denied. You have to create a system that automatically decides whether to grant a loan or not to grant a loan.

Importing libraries

from sklearn import tree
from sklearn import metrics
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

Reading data file through pandas

def readFileThroughPandas(filename):
    # Reads the entire data file
    data = pd.read_csv(filename)
    
    att = data[["Marital Status","Kids","Annual Household Salary","Loan Amount","Car owner", "Education Level"]]
    lab = data["Loan Granted"]
    
    # Data normalization not required for decision trees. Why?
    
    return(att,lab)

(att,lab) = readFileThroughPandas("loanacceptance.csv")

# shape of the variables
print(att.shape)
print(lab.shape)

# Use the first 400 rows for training and the remaining rows for testing

x_train = att.iloc[0:400]
y_train = lab.iloc[0:400]

x_test = att.iloc[400:]
y_test = lab.iloc[400:]

# Alternatively use the following code to choose the rows randomly
# x_train, x_test, y_train, y_test = train_test_split(att, lab, test_size = 0.20)

(500, 6)
(500,)

Fitting a decision tree

# Note that only 80% of the dataset is being used for training
clf = tree.DecisionTreeClassifier()
clf.fit(X=x_train,y=y_train)

DecisionTreeClassifier()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Printing performance metrics

# returns accuracy
print("Training accuracy",clf.score(X=x_train,y=y_train))
print("Testing accuracy",clf.score(X=x_test,y=y_test))

y_predicted = clf.predict(x_test)

# Following is an alternative way to get the accuracy scores
# print("Testing accuracy",metrics.accuracy_score(y_test,y_predicted))

Training accuracy 1.0
Testing accuracy 0.9

Always look at the confusion matrix

m = metrics.confusion_matrix(y_test,y_predicted,labels=clf.classes_)
print(m)

[[24  6]
 [ 4 66]]

# Better visualization of a confusion matrix
disp = metrics.ConfusionMatrixDisplay(confusion_matrix=m,display_labels=clf.classes_)
disp.plot()
plt.show()