I am using the bank marketing dataset from the UCI ML repo to build an example of a big data storage system along with ETL workflows and Machine Learning models. I would like to create more data so I can feed it to the storage solution pretending it is new "fresh" data from different time periods.
I know there are techniques to create more data by adding noise while at the same time maintaining the same underlying structure. Could anybody suggest some that would apply to this dataset and add a motivation?
I am dealing with just numeric features of type categorical, continuous and binary (no image or text data). I don't think this matters but in case it does, this is a binary classification problem.
Thanks for all your inputs!