Which metric is used here to calculate wcss?

Question

I have attached the code below.

I want to know whether whether Eucledian method or inertia method is used to calculate WCSS here.

Code:

K-Means Clustering

Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

Importing the dataset

dataset = pd.read_csv('crime.csv')

X = dataset.iloc[:, [1,2,3,4,5,6,7,12]].values

Feature Scaling

from sklearn.preprocessing import StandardScaler

sc_X = StandardScaler()

X = sc_X.fit_transform(X)

Using the elbow method to find the optimal number of clusters

from sklearn.cluster import KMeans

wcss = []

for i in range(1, 21):

    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)

    kmeans.fit(X)

    wcss.append(kmeans.inertia_)

plt.plot(range(1, 21), wcss)

plt.title('The Elbow Method')

plt.xlabel('Number of clusters')

plt.ylabel('WCSS')

plt.show()

Fitting K-Means to the dataset

kmeans = KMeans(n_clusters = 6, init = 'k-means++', random_state = 42)

y_kmeans = kmeans.fit_predict(X)


from sklearn.decomposition import KernelPCA

kpca = KernelPCA(n_components = 2, kernel = 'rbf')

X = kpca.fit_transform(X)

Visualising the clusters

plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red')

plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue')

plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 100, c = 'green')

plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 100, c = 'cyan')

plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 100, c = 'magenta' )

plt.scatter(X[y_kmeans == 5, 0], X[y_kmeans == 5, 1], s = 100, c = 'black' )




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

plt.title('Clusters of areas')

plt.xlabel('factor1')

plt.ylabel('factor2')

plt.legend()

plt.show()