So firstly, if the model's performance was not as expected, we're going to diagnose how the issue might have occurred. We're going to firstly deal with the datasets. We need to check if all the values in the datasets were consistent or not. Once we've done that, we're going to apply further layers and further assess the model being applied to the data. We're going to check whether there was a risk of overfitting or underfitting. If there was a risk of underfitting, then we're going to lessen the amount of layers in the specific model. And if there was a risk of underfitting, we're going to increase our dataset and we're going to increase the layers as well to train the model better. Then we're going to apply various validation and testing sets to see our results once again. And if not, then we're also going to explore various techniques such as bagging and assembling to make our data more consistent. And if we are having a lack of datasets, we can also generate further datasets using GAMs or other various methods.
