I am using keras neural networks for a binary classification task. I have a large dataset with 19 columns. Each of these columns is of binary type, and therefore the entire dataset is just 1s and 0s, like so (obviously this is not the entire dataset, which has 19 columns and many more rows):
I want to make 19 predictions, one for each column, where the other 18 are used as independent variables. So essentially, first I'll make col 1 the target, using the other 18 columns to predict it. Then I'll make col 2 the target and use the other 18 to predict it etc etc...until column 19. Is there any way to do this effectively without making 19 separate models for each column? Thanks!