data_visualization.py

Y_pred = np.round(model.predict(X_test),2)

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(Y_test, Y_pred)

rmse = np.sqrt(mse)
rmse

# Scaling back to the original scale
d = scaled_dataset[:8760,:]
print('dummy',d.shape)
print('Y_pred',Y_pred.shape)
Y_predicted = np.concatenate((Y_pred,d[:8760,1:]), axis =1)
print('concat y_pred',Y_pred.shape)
Y_tested = np.concatenate((Y_test, d[:8760,1:]), axis = 1)
print('concat Y_test', Y_test.shape)

Y_predicted = scaler.inverse_transform(Y_predicted)
Y_tested = scaler.inverse_transform(Y_tested)
Y_predicted = Y_predicted[:,0:1]
Y_tested = Y_tested[:,0:1]
print('Y_tested', Y_tested.shape)
print('Y_predicted', Y_predicted.shape)

import matplotlib.pyplot as plt

plt.plot(Y_predicted[:100,:], color= 'green')
plt.plot(Y_tested[:100,:] , color = 'red')
plt.title("Air Pollution Prediction (Multivariate)")
plt.xlabel("Date")
plt.ylabel("Pollution level")
plt.show()

model.save("drive/MyDrive/api/air_pollution_forecasting_model")