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))