ml9

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_iris
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering
from sklearn.decomposition import PCA

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Standardize the data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Generate the linkage matrix
linked = linkage(X_scaled, method='ward')

# Plot the dendrogram
plt.figure(figsize=(10, 7))
dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)
plt.title('Dendrogram for Hierarchical Clustering')
plt.xlabel('Samples')
plt.ylabel('Euclidean Distance')
plt.show()

# Apply Agglomerative Clustering
hc = AgglomerativeClustering(n_clusters=3, affinity='euclidean', linkage='ward')
y_hc = hc.fit_predict(X_scaled)

# Reduce dimensions to 2D using PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)

# Plot the clusters
plt.figure(figsize=(8, 5))
plt.scatter(X_pca[y_hc == 0, 0], X_pca[y_hc == 0, 1], s=50, c='red', label='Cluster 1')
plt.scatter(X_pca[y_hc == 1, 0], X_pca[y_hc == 1, 1], s=50, c='blue', label='Cluster 2')
plt.scatter(X_pca[y_hc == 2, 0], X_pca[y_hc == 2, 1], s=50, c='green', label='Cluster 3')
plt.title('Clusters of Iris Data')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend()
plt.show()