DM8

import numpy as np
import matplotlib.pyplot as mtp
import pandas as pd
dataset = pd.read_csv('CC GENERAL.csv')
dataset
x = dataset.iloc[:, [3, 4]].values
print(x)
from sklearn.cluster import KMeans
wcss_list= []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42)
kmeans.fit(x)
wcss_list.append(kmeans.inertia_)
mtp.plot(range(1, 11), wcss_list)
mtp.title('The Elobw Method Graph')
mtp.xlabel('Number of clusters(k)')
mtp.ylabel('wcss_list')
mtp.show()
kmeans = KMeans(n_clusters=3, init='k-means++', random_state= 42)
y_predict= kmeans.fit_predict(x)
mtp.scatter(x[y_predict == 0, 0], x[y_predict == 0, 1], s = 100, c = 'blue', label
= 'Cluster 1') #for first cluster
mtp.scatter(x[y_predict == 1, 0], x[y_predict == 1, 1], s = 100, c = 'green',
label = 'Cluster 2') #for second cluster
mtp.scatter(x[y_predict== 2, 0], x[y_predict == 2, 1], s = 100, c = 'red', label = 'Cluster 3')
#for third cluster
mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c =
'yellow', label = 'Centroid')
mtp.title('Clusters of Credit Card')
mtp.xlabel('V3')
mtp.ylabel('V4')
mtp.legend()
mtp.show()