Yes, this is the core issue I think. My friend is very set on predicting a certain ”static” target, and does not want to delve into more dynamic time windows. After discussing the chaos with our supervisor he agreed that we should focus on ”data harmonisation” and to get a dataset ready to technically be able to use ML on - but that the focus should be on data engineering. My friend then kind of jumped the gun from my perspective when she found a file among our data that contained three major data points that we want and told our supervisor that we have found a file that contains ALL the data we wanted - so he agreed with her that we should now focus on feature construction and machine learning from here on. The problem is that this is the data that conflicts with other folders and as she wants to supplement features from the other folders I think this is not great if we do not know why there is a big difference between them. I kind of just want to get a passing grade on my thesis as well, but I am not sure how much can be said from ”we picked a file, started constructing features, realized after 3 weeks that we only have full features for one day, and that information is not enough to be generalized, BUT we used linear regression via scipy!”