import numpy as np
import  matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

from sklearn.datasets import make_blobs
from sklearn import  metrics

n_samples = 1500
x, y = make_blobs(n_samples=n_samples, centers=4, random_state=170)

x = StandardScaler().fit_transform(x)

KMeans = KMeans(n_clusters=4, n_init='auto', random_state=170)
KMeans.fit(x)

plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.scatter(x[:, 0], x[:, 1], c='r')
plt.title("Before clustering")
plt.subplot(122)
plt.scatter(x[:, 0], x[:, 1], c=KMeans.labels_)
plt.title("After clustering")
plt.show()