Q5: Implement an agglomerative hierarchical clustering algorithm using an appropriate dataset. Visualize the dendrogram and report cluster assignments.

"""Cluster customers with agglomerative hierarchical clustering."""

import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import StandardScaler


data = pd.read_csv(Path(__file__).with_name("Mall_Customers.csv"))
features = data[["Age", "Annual Income (k$)", "Spending Score (1-100)"]]
scaled = StandardScaler().fit_transform(features)

tree = linkage(scaled, method="ward")
dendrogram(tree)
plt.title("Customer Dendrogram")
plt.xlabel("Customers")
plt.ylabel("Distance")
plt.show()

model = AgglomerativeClustering(n_clusters=5, linkage="ward")
data["Cluster"] = model.fit_predict(scaled)
print(data[["CustomerID", "Age", "Annual Income (k$)", "Spending Score (1-100)", "Cluster"]].head(20))