from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor

X_train, X_test, y_train, y_test =  train_test_split(
        X_scaled, y_data, test_size=0.20, random_state=1)

rgr = MLPRegressor(hidden_layer_sizes=(100, ),
                   activation='logistic',
                   solver='sgd',
                   alpha=0.0001,
                   batch_size=8,
                   learning_rate='constant',
                   learning_rate_init=0.001,
                   power_t=0.5,
                   max_iter=100,
                   shuffle=True,
                   random_state=None,
                   tol=0.001,
                   verbose=True,
                   warm_start=False,
                   momentum=0.9,
                   nesterovs_momentum=True,
                   early_stopping=False,
                   validation_fraction=0.25,
                   )

for i in list(range(100)):
    rgr.partial_fit(X_train, y_train)

y_predicted = rgr.predict(X_test)`