import warnings
from sklearn.exceptions import ConvergenceWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.metrics import accuracy_score

# Load the iris dataset
iris = load_iris()

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(,, test_size=0.2, random_state=42)

# Create the individual classification models
dt = DecisionTreeClassifier()
lr = LogisticRegression()
knn = KNeighborsClassifier()

# Create the voting classifier by combining the individual models
vc = VotingClassifier(estimators=[('dt', dt), ('lr', lr), ('knn', knn)])

# Ignore the convergence warning
warnings.filterwarnings("ignore", category=ConvergenceWarning)

# Train the voting classifier on the training data, y_train)

# Make predictions on the test data using the voting classifier
y_pred = vc.predict(X_test)

# Evaluate the performance of the voting classifier
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)