from sklearn.model_selection import KFold
from sklearn import datasets
from sklearn.linear_model import LinearRegression
diabetes = datasets.load_diabetes() # Load dataset
X = diabetes.data  # Split the data into features and target
y = diabetes.target
# Create a KFold object with shuffle=True
kf = KFold(n_splits=5, shuffle=True, random_state=42)
# Iterate through the splits and fit a model
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    model = LinearRegression()
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)
    print(score)