# Hierarchical Clustering

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

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

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

# Fitting hierarchical clustering to the mall dataset
from sklearn.cluster import AgglomerativeClustering
hc = AgglomerativeClustering(n_clusters = 5, affinity ='euclidean',linkage = 'ward')
y_hc = hc.fit_predict(X)

#Visualising the clusters

plt.scatter(X[y_hc==0,0],X[y_hc==0,1],s=100,c='red',label='Careful')
plt.scatter(X[y_hc==1,0],X[y_hc==1,1],s=100,c='blue',label='Standard')
plt.scatter(X[y_hc==2,0],X[y_hc==2,1],s=100,c='green',label='Target')
plt.scatter(X[y_hc==3,0],X[y_hc==3,1],s=100,c='yellow',label='Careless')
plt.scatter(X[y_hc==4,0],X[y_hc==4,1],s=100,c='brown',label='Sensible')
plt.title("Clusters of clients")
plt.xlabel("Annual Income")
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()