import sklearn.datasets
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split

We first need to gather the data

For this we will load the moon data and apply an KNN Algorithm

X,y = sklearn.datasets.make_moons(n_samples=100,noise=0.2,random_state=0)
plt.scatter(x=X[:,0],y=X[:,1],c=y)
plt.show()

Now we apply KNN to the data and visualize the prediction regions

X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)

model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train,y_train)

KNeighborsClassifier()

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from sklearn.inspection import DecisionBoundaryDisplay
disp = DecisionBoundaryDisplay.from_estimator(estimator=model, X=X_test)
scatter = disp.ax_.scatter(X[:, 0], X[:, 1], c=y, edgecolors="k")
disp.ax_.legend(
        scatter.legend_elements()[0],
        ["Class 0", "Class 1"],
        loc="lower left",
        title="Classes",
)
plt.show()