neural networks hyperparameter tuning

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
import numpy as np
import warnings
warnings.filterwarnings('ignore')


X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.25, random_state=42)
scaler = MinMaxScaler() 
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
model = MLPClassifier()


param_grid = {'hidden_layer_sizes':[250,300],
             'activation':['tanh', 'relu'],
             'solver':['sgd', 'adam'],
             'alpha' : [0.1,0.2],
             'learning_rate':  ['adaptive','invscaling'] }

gridsearchcv = GridSearchCV(model, param_grid, cv = 3, verbose=True, n_jobs=-1)
best_parameters = gridsearchcv.fit(X_train, y_train)
best_parameters