Predict the label of a data point by :

• Looking at the k closest labeled data points
• taking a majority vote

from sklearn.neighbors import KNeighborsClassifier
X = churn_df[['Total_day_charge', 'Total_eve_charge']].values
Y = churn_df["churn"].values

print(X.shape,Y.shape)

knn = KNeighborsClassifier(n_neighbors=15)
knn.fit(X,Y)

Predicting on Unlabeled Data -

X_new = np.array([[56.8,17.5],
									[24.4,24,1],
									[50.1,10.9]])
									
print(x_new.shape)

predictions = knn.predict(x_new)

#prints out the churn for observations
print('Predictinos : {}'.format(predictions))