SupportVectorMachine.py

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC # Support Vector Classifier
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

cancer = load_breast_cancer()
X, y = cancer.data, cancer.target

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

svm_model = SVC(kernel='linear', random_state=42) # Using a linear boundary
svm_model.fit(X_train, y_train)

predictions = svm_model.predict(X_test)

accuracy = accuracy_score(y_test, predictions)
print(f"SVM Model Accuracy: {accuracy:.2f}")

# Display  2x2 confusion matrix
cm = confusion_matrix(y_test, predictions)
sns.heatmap(cm, annot=True, fmt='d', cmap='Reds',
            xticklabels=cancer.target_names, yticklabels=cancer.target_names)
plt.xlabel('Predicted Label')
plt.ylabel('True Label')
plt.title('SVM Confusion Matrix')
plt.show()