create decision tree classifier in python

main.py
# Import the required libraries
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create the decision tree classifier
clf = DecisionTreeClassifier()

# Train the classifier on the training data
clf.fit(X_train, y_train)

# Test the classifier on the testing data
y_pred = clf.predict(X_test)

# Calculate the accuracy of the classifier
acc = clf.score(X_test, y_test)
print('Accuracy:', acc)
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