Accuracy and Loss in MLP
It is hard to tell if it is the model or the signal/noise ratio in data. One sanity check could be to see how well a human would perform with the most important features.

I don't think you want to switch to a sequence model, I have a hard time seeing it do better for this data. Also, don't switch to regression.

Some suggestions:


Try a gradient boosting model, like LightGBM or XGBoost. It is really good on tabular data.
Do feature engineering and try to come up with features that might be good but does not exist in the data. You could aggregate to get the sequential information in there, but you already seem to be doing that to some extent.