1.py

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+from sklearn.datasets import load_iris
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+
+# Load the Iris dataset (or you can use your own dataset)
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create an instance of the DecisionTreeClassifier
+clf = DecisionTreeClassifier()
+
+# Train the decision tree classifier
+clf.fit(X_train, y_train)
+
+# Make predictions on the testing set
+y_pred = clf.predict(X_test)
+
+# Evaluate the accuracy of the model
+accuracy = metrics.accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)