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import numpy as np

import pandas as pd

from matplotlib import pyplot as plt

from sklearn.datasets.samples_generator import make_blobs

from sklearn.cluster import KMeans

X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

plt.scatter(X[:,0], X[:,1])

wcss = []

for i in range(1, 11):

kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)

kmeans.fit(X)

wcss.append(kmeans.inertia_)

plt.plot(range(1, 11), wcss)

plt.title('Elbow Method')

plt.xlabel('Number of clusters')

plt.ylabel('WCSS')

plt.show()

kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)

pred_y = kmeans.fit_predict(X)

plt.scatter(X[:,0], X[:,1])

plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')

plt.show()