import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('Mall_Customers.csv')
X = dataset.iloc[:,3:5].values

import scipy.cluster.hierarchy as sch

dendrogram  = sch.dendrogram(sch.linkage(X, method = 'ward'))
plt.show()

from sklearn.cluster import AgglomerativeClustering
hc  =  AgglomerativeClustering(n_clusters = 5 , affinity = 'euclidean', linkage = 'ward')
y_hc = hc.fit_predict(X)


plt.scatter(X[y_hc==0,0],X[y_hc==0,1], s=100, c = 'red' , label = 'cluster1')
plt.scatter(X[y_hc==1,0],X[y_hc==1,1], s=100, c = 'blue' , label = 'cluster2')
plt.scatter(X[y_hc==2,0],X[y_hc==2,1], s=100, c = 'green' , label = 'cluster3')
plt.scatter(X[y_hc==3,0],X[y_hc==3,1], s=100, c = 'cyan' , label = 'cluster4')
plt.scatter(X[y_hc==4,0],X[y_hc==4,1], s=100, c = 'magenta' , label = 'cluster5')
plt.legend()
plt.show()