I'm a DS student. I have like 30.000 of bank statements, all labeled with a specific category(cat1, cat2, ...). With that data I'm trying to train a classification model but I found several problems:
- The category that has more statements has 10k, the one with less 100 (unbalanced dataset)
- Many rows are repeated with the same text ( for example "Buy for a total of X$ in the supermarket Y", but the repeated rows appears in each label)
- Should I train with repeated statements? if I do so the model will be trained for some specific cases and the predicted results will not be real because for example if I have repeated statements with the same label:
A, B, B, B, B, B (labels)
and I take only one row with label A and B, since the statement will be repeated the accuracy predicting the others B's will be 100%.
- If I do not train with repeated statements am I not deleting important information about my data and can affect the prediction later?
- Should I undersampling or oversampling? It's correct for this kind of data? I will have more repeated rows in the case of oversampling, and undersampling I'll lose a lot of information.
I do not know if what I'm thinking has some sense. I really need some tips in order to train my data.