K Nearest Neighbors

We use a classification model to predict which customers will default on their credit card debt.

Data

To learn more about the data and all of the data preparation steps, take a look at this page. Here, we simply import a Python script which includes all of the necessary steps.

from data_prep_credit import * 

Model

from sklearn import neighbors

clf = neighbors.KNeighborsClassifier(n_neighbors=2)
y_pred = clf.fit(X_train, y_train).predict(X_test)

Confusion matrix

from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
import matplotlib.pyplot as plt

cm = confusion_matrix(y_test, y_pred)

disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                              display_labels=clf.classes_)
disp.plot()
plt.show()

Classification report

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred, digits=3))

              precision    recall  f1-score   support

         0.0      0.975     0.996     0.985      2909
         1.0      0.571     0.176     0.269        91

    accuracy                          0.971      3000
   macro avg      0.773     0.586     0.627      3000
weighted avg      0.963     0.971     0.963      3000