lab10

 import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

import warnings

from sklearn.datasets import load_breast_cancer

from sklearn.cluster import KMeans

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

from sklearn.metrics import confusion_matrix, classification_report

warnings.filterwarnings("ignore", category=UserWarning)

warnings.filterwarnings("ignore", category=FutureWarning)

data = load_breast_cancer()

X = data.data

y = data.target

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)

y_kmeans = kmeans.fit_predict(X_scaled)

print("Confusion Matrix:")

print(confusion_matrix(y, y_kmeans))

print("\nClassification Report:")

print(classification_report(y, y_kmeans))

pca = PCA(n_components=2)

X_pca = pca.fit_transform(X_scaled)

df = pd.DataFrame(X_pca, columns=['PC1', 'PC2'])

df['Cluster'] = y_kmeans

df['True Label'] = y

plt.figure(figsize=(8, 6))

sns.scatterplot(

    data=df, x='PC1', y='PC2', hue='Cluster', palette='Set1',

    s=100, edgecolor='black', alpha=0.7, marker='o'

)

plt.title('K-Means Clustering of Breast Cancer Dataset')

plt.xlabel('Principal Component 1')

plt.ylabel('Principal Component 2')

plt.legend(title="Cluster")

plt.show()

plt.figure(figsize=(8, 6))

sns.scatterplot(

    data=df, x='PC1', y='PC2', hue='True Label', palette='coolwarm',

    s=100, edgecolor='black', alpha=0.7, marker='o'

)

plt.title('True Labels of Breast Cancer Dataset')

plt.xlabel('Principal Component 1')

plt.ylabel('Principal Component 2')

plt.legend(title="True Label")

plt.show()

plt.figure(figsize=(8, 6))

sns.scatterplot(

    data=df, x='PC1', y='PC2', hue='Cluster', palette='Set1',

    s=100, edgecolor='black', alpha=0.7, marker='o'

)

centers = pca.transform(kmeans.cluster_centers_)

plt.scatter(centers[:, 0], centers[:, 1], s=200, c='red', marker='X', label='Centroids')

plt.title('K-Means Clustering with Centroids')

plt.xlabel('Principal Component 1')

plt.ylabel('Principal Component 2')

plt.legend(title="Cluster")

plt.show()