hyperparameter tuning

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

#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

svm_clf = SVC(kernel='linear')

param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}
grid_search = GridSearchCV(svm_clf, param_grid, cv=5, scoring='accuracy')

grid_search.fit(df, y)
best_params = grid_search.best_params_
print(best_params)