a9

#program 9
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering
# Importing the dataset
dataset = pd.read_csv('P9Data.csv')
X = dataset.iloc[:, [3,4]].values
# Dendrogram
sch.dendrogram(sch.linkage(X, method='ward'))
plt.title('Dendrogram')
plt.xlabel('Customers')
plt.ylabel('Euclidean distances')
plt.show()
# Fitting Hierarchical Clustering
hc = AgglomerativeClustering(n_clusters=5, linkage='ward')
y_hc = hc.fit_predict(X)
# Visualizing the clusters
colors = ['r', 'b', 'g', 'c', 'm']
for i in range(5):
 plt.scatter(X[y_hc == i, 0], X[y_hc == i, 1], s=100, c=colors[i],
label=f'Cluster {i+1}')

plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show()