cross validation

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

#Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.1, random_state=42)

#Train a logistic regression model
model = LogisticRegression(max_iter=1000)

#Cross-Validation
cv_scores = cross_val_score(model, df, y, cv=5, scoring='accuracy')
mean_cv_score = cv_scores.mean()
print(mean_cv_score)