You'll probably want to scrape points directly from historic blockchain transactions such as ones from 2017 and 2018. I wouldn't simply randomly generate points if the purpose is to guess the polarity of Y in public keys that are used in real life, there's no use to that.

Given that each block averages a few thousand transactions, you'll want to train on transactions from a few hundred blocks, which'll result in about a few million or hundred thousand compressed points of training data. And that shouldn't take more than a few dozen MB if they are stored as text, one compressed address per line.

Remembering that an epoch is 2016 blocks long, you can either choose to target trends in Y polarity for a single epoch (given that most bitcoin "politics" occur right at the beginning and usually last for the epoch duration), or you can sample blocks from different epochs and try to get a general idea of Y polarity throughout history.

I'm not really sure how many hidden layers would be necessary for this kind of thing, but maybe one of the answers at https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw will help.