#1.Start by visualizing some data points:
import matplotlib.pyplot as plt
x = [4, 5, 10, 4, 3, 11, 14 , 6, 18, 12]
y = [21, 19, 24, 17, 16, 25, 24, 22, 21, 21]
plt.scatter(x, y)
plt.show()
.png)
from sklearn.cluster import KMeans
data = list(zip(x, vy))
inertias = []
for i in range(1,11):
kmeans = KMeans(n_clusters=i)
kmeans.fit(data)
inertias.append(kmeans.inertia_)
plt.plot(range(1,11), inertias, marker='o0")
plt.title('Elbow method")
plt.xlabel('Number of clusters")
plt.ylabel( Inertia’)
plt.show()
.png)
#3,The elbow method shows that 2 is a good value for K, so we retrain and visualize the
kmeans = KMeans(n_clusters=2)
kmeans.fit(data)
plt.scatter(x, y, c=kmeans.labels_)
plt.show()
.png)
