text_plain_output_1.png
from sklearn.neural_network import MLPRegressor

from sklearn.neural_network import MLPRegressor

from sklearn.preprocessing import MinMaxScaler

from sklearn.preprocessing import MinMaxScaler

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

scaler = StandardScaler()
scaler.fit(X2_train)
X2_train = scaler.transform(X2_train)
X2_test = scaler.transform(X2_test)

import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X_train = scaler_mm.fit_transform(X_train)
X_test = scaler_mm.transform(X_test)

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(max_iter=300)
reg.fit(X_train, y_train)
reg.score(X_test, y_test)

import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
scaler_mm = MinMaxScaler()
X2_train = scaler_mm.fit_transform(X2_train)
X2_test = scaler_mm.transform(X2_test)

from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
reg = MLPRegressor(max_iter=300)
reg.fit(X2_train, y2_train)
reg.score(X2_test, y2_test)