this might be very beginner's question. I'm working on Kaggle's HomeCredit Default Risk problem which has among others dataset train, test and submission files as can be seen in the link provided. The test dataset does not contain TARGET feature and submission has ID and TARGET columns where TARGET has fixed value 0.5.
I'd normaly split the train dataset and used it to train and score a model and than created a submission file based on the test dataset. Thus, I don't understand what I am supposed to do with the already existing submission file.
Futher, when I score a model based on splitted train data, I'm getting unexpected high values which can't be right. I did before OHE-encoding, standardization and PCA, it that might have some impact on the model accuracy result.
Insights would be apprecited.