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

#Import dataset
dataset = pd.read_csv('Mall_Customers.csv')
X = dataset.iloc[:, [3, 4]].values

#Using the dendrogram
import scipy.cluster.hierarchy as sch
dendogram = sch.dendrogram(sch.linkage(X, method = 'ward'))
plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()

#Choosing right k cluster
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')
y_pred = hc.fit_predict(X)

#Visualizing the results
plt.scatter(X[y_pred == 0, 0], X[y_pred == 0, 1], s = 100, c = 'red', label = 'Careful')
plt.scatter(X[y_pred == 1, 0], X[y_pred == 1, 1], s = 100, c = 'blue', label = 'Standard')
plt.scatter(X[y_pred == 2, 0], X[y_pred == 2, 1], s = 100, c = 'green', label = 'Target')
plt.scatter(X[y_pred == 3, 0], X[y_pred == 3, 1], s = 100, c = 'cyan', label = 'Careless')
plt.scatter(X[y_pred == 4, 0], X[y_pred == 4, 1], s = 100, c = 'magenta', label = 'Sensible')
plt.xlabel('Annual Income')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()