plt.plot(x, regressor.predict(x), color='r')
'''
Housing Prices
'''
base = pd.read_csv('C:\\Users\\André Viniciu\\Documents\\Python_ML\\Curso\\Secao 14 - Regressao Linear\\house_prices.csv')
# Values
x = base.iloc[:, 3:19].values
y = base.iloc[:, 3].values
# Escalonamento
scaler_x = StandardScaler()
x = scaler_x.fit_transform(x)
scaler_y = StandardScaler()
y = scaler_y.fit_transform(y.reshape(-1, 1))
# Split
x_treinamento, x_teste, y_treinamento, y_teste = train_test_split(x, y, test_size=0.3, random_state=0)
# Regressor
regressor = MLPRegressor(hidden_layer_sizes=(9, 9, 9), verbose=True)
regressor.fit(x_treinamento, y_treinamento)
regressor.score(x_treinamento, y_treinamento)
regressor.score(x_teste, y_teste)
previsoes = scaler_y.inverse_transform(regressor._predict(x_teste))
