import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
import seaborn as sns
# Load the Iris dataset
iris = datasets.load_iris()
X = iris.data[:, :2] # Consider only the first two features for visualization
y = iris.target
# Split the data into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create SVM and Logistic Regression classifiers
svm_classifier = SVC(kernel='linear', C=1, random_state=42)
logistic_regression = LogisticRegression(multi_class='multinomial', solver='lbfgs', C=1, random_state=42)
# Fit the models to the training data
svm_classifier.fit(X_train, y_train)
logistic_regression.fit(X_train, y_train)
# Create a meshgrid for visualization
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, 0.02), np.arange(y_min, y_max, 0.02))
# Predict the class labels for the meshgrid points
Z_svm = svm_classifier.predict(np.c_[xx.ravel(), yy.ravel()])
Z_svm = Z_svm.reshape(xx.shape)
Z_logistic = logistic_regression.predict(np.c_[xx.ravel(), yy.ravel()])
Z_logistic = Z_logistic.reshape(xx.shape)
# Create subplots to visualize both models
plt.figure(figsize=(12, 5))
# SVM decision boundary
plt.subplot(1, 2, 1)
plt.contourf(xx, yy, Z_svm, cmap=plt.cm.YlGnBu, alpha=0.8)
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=iris.target_names[y], palette="Set1")
plt.title("SVM Decision Boundary")
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
# Multinomial Logistic Regression decision boundary
plt.subplot(1, 2, 2)
plt.contourf(xx, yy, Z_logistic, cmap=plt.cm.YlGnBu, alpha=0.8)
sns.scatterplot(x=X[:, 0], y=X[:, 1], hue=iris.target_names[y], palette="Set1")
plt.title("Multinomial Logistic Regression Decision Boundary")
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
plt.tight_layout()
plt.show()
