def plot_decision_boundary(X, y, model, scaler):
h = 0.02 # Step size for mesh
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Predict on mesh points
Z = model.predict(scaler.transform(np.c_[xx.ravel(), yy.ravel()]))
Z = Z.reshape(xx.shape)
# Plot decision boundary and data points
plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.3)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors='k')
plt.xlabel(data.feature_names[0])
plt.ylabel(data.feature_names[1])
plt.title('SVM Decision Boundary')
plt.show()
plot_decision_boundary(X_train, y_train, svm_classifier, scaler)