# import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# load iris dataset
iris = 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=0.2, random_state=42)

# create a Logistic Regression model
lr_model = LogisticRegression()

# fit the model on training data
lr_model.fit(X_train, y_train)

# make predictions on the testing data
y_pred = lr_model.predict(X_test)

# evaluate model accuracy using accuracy_score
acc_score = accuracy_score(y_test, y_pred)
print("Accuracy:", acc_score)