import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

iris = load_iris()  # Load the iris dataset
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)
model = DecisionTreeClassifier(random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
cm = confusion_matrix(y_test, y_pred)   

# Plot the confusion matrix using seaborn
sns.heatmap(cm, annot=True, cmap='Blues', fmt='g', xticklabels=iris.target_names, yticklabels=iris.target_names)
plt.title('Confusion Matrix')
plt.xlabel('Predicted Labels')
plt.ylabel('Actual Labels')
plt.show();