random forests hyperparameter tuning

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
import numpy as np

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 = RandomForestClassifier()


param_grid = {'n_estimators':[200,250],
        'max_depth':[3,5,7],
          'min_samples_leaf':[3,5,10],
          'min_samples_split':[8,10,12],
          'criterion':['gini','entropy']}

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