from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Step 1: Load the Iris dataset
X, y = load_iris(return_X_y=True)

# Step 2: Split data into training (80%) and testing (20%)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 3: Initialize Decision Tree Classifier
clf = DecisionTreeClassifier(random_state=42)

# Step 4: Train the model
clf.fit(X_train, y_train)

# Step 5: Predict and evaluate
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred) * 100
print(f"Decision Tree model accuracy: {acc:.2f}%")

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