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CharlesRajendran/elbow-method.py

Created March 17, 2018 11:09
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elbow-method.py
X = data.iloc[:, 1:3]
# use elbow mwthod to find optimal number of clusters
from sklearn.cluster import KMeans
# with in cluster sum of squares
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters =i, init="k-means++", max_iter=300, n_init=10)
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()
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