X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y)
print(neigh.predict([[1.1]]))
print(neigh.predict_proba([[0.9]]))
# Import KNeighborsClassifier from sklearn.neighbors
from sklearn.neighbors import KNeighborsClassifier
# Create arrays for the features and the response variable
y = df['features'].values
X = df.drop('features', axis=1).values
# Create a k-NN classifier with 6 neighbors
knn = KNeighborsClassifier(n_neighbors=6)
# Fit the classifier to the data
knn.fit(X, y)
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=8, p=2,
weights='uniform')