Elbow Curve Method for finding number of cluster(K) for clustering with Plot

K in KMeans

# Elbow Curve Method for finding number of cluster(K) for clustering.
# Taking initially range is from (1,11).


x = df.iloc[:,[0,1,2,3]].values

wcss = []

for i in range(1,11): # we are taking 1,10 values as initial centroids.
  kmeans = KMeans(n_clusters = i, init = 'k-means++',max_iter = 300, n_init = 10, random_state = 0)
  kmeans.fit(x)
  wcss.append(kmeans.inertia_) # appending the within cluster sum of squares 
  
 fig = plt.figure(figsize=(10,12))
plt.rc('font', size=15) 
plt.subplots_adjust(bottom = 0, left = 0, top = 1, right = 1)
sub1 = fig.add_subplot(2,2,(1,2)) # two rows, two columns, first cell
sub1.plot(range(1,11),wcss)
sub1.set_title('The Elbow Method')
sub1.set_xlabel('Number of Cluster')
sub1.set_ylabel('WCSS')

sub2 = fig.add_subplot(2,2,3) # two rows, two colums, combined third and forth cell
sub2.set_xlim(2,5)
sub2.set_ylim(0,150000)
sub2.plot(range(1,11),wcss)
sub2.set_title('The Elbow Method')
sub2.set_xlabel('Number of Cluster')
sub2.set_ylabel('WCSS')
con1 = ConnectionPatch(xyA=(1.7,140000),xyB=(2,140000),axesA=sub1,axesB=sub2,coordsA="data",coordsB="data")
con2 = ConnectionPatch(xyA=(4,22000),xyB=(5,140000),axesA=sub1,axesB=sub2,coordsA="data",coordsB="data")

sub2.add_artist(con1)
sub2.add_artist(con2)

plt.show()