def sframe(frame):
    sf = SFrame(frame)
    sf.explore()
    sf.show()
    return
    if multivariate:
        visualisation(y_pred=y_pred[:, 0].reshape(-1, 1),
                      Y_test=Y_test[:, 0].reshape(-1, 1),
                      y_difference=y_difference[:, 0].reshape(-1,
                                                              1))  # Insertion
        visualisation(y_pred=y_pred[:, 1].reshape(-1, 1),
                      Y_test=Y_test[:, 1].reshape(-1, 1),
                      y_difference=y_difference[:, 1].reshape(-1,
                                                              1))  # Deletion
    else:
        visualisation(y_pred, Y_test, y_difference)

    #Looking into sframe as an alternative to pandas, has s3 support
    #Tensorflow also has s3 and GCP support if you install from source and enable it
    sf = SFrame(build_csv(build_data(sample)[5], build_data(sample)[7]))
    sf.explore()
    sf.show()

else:
    #Neural Network architecture

    def build_regressor():

        #Initialising neural network
        regressor = Sequential()

        #Input layer and first hidden layer with dropout
        regressor.add(
            Dense(units=10,
                  kernel_initializer='uniform',
                  activation='relu',