Note that I am cross-posting this answer because the question is cross-posted, and one of the questions will likely be deleted. If this is a breach of etiquette, please let me know. Cross-post is here.
This question has two parts, at least implicitly. The second part is, generally speaking, how to train and predict from neural networks (in Python). The first part is (implicitly) how do we build a model that predicts (correctly) which person won a presidency based on demographics.
The answer to the second question is quite easy to state, but harder to do. It boils down to the rather unhelpful statement: learn about neural networks, and use that knowledge. Google for tutorials on neural networks. I would make recommendations, but I don't know your mathematical, statistical, and computer science background, and thus cannot really make suggestions.
The first question, however, I can answer, although likely not in the way you'd like. Just using the data you've listed, it's highly unlikely you'll get any sort of accuracy out of your neural networks, or any other model you fit. Why? For several reasons.
The first is that unless you can always classify the president into one of two (or more) distinct groups, as you can in the US (Democrat, Republican), your data are changing all the time. You can't classify anything if it doesn't have a class, and that's difficult in some countries.
Secondly, even if there are distinct classes, demographic data is likely not a set of good predictors. Demographic data changes depending on how good the local governance is, as well as the overall governance. You might, might be able to code that in somehow. But demographic data also changes due to a lot of other things. When it comes down to voting, people choose to vote based on a bunch of things, and their demographic status is only one factor.
The third reason is more a caution than anything. Neural networks and all kinds of machine learning/statistical modelling methods can give good predictions with the right kind of data. Something like predicting a president is relatively difficult, especially in smaller countries. (Nate Silver did it well in the US, but there was a lot more data there.) And even if you've got great data and a great model, there's invariably something you miss. George Box said it right: "All models are wrong, but some are useful." When you mention predicting correctly, all sorts of alarm bells go off in my head. The problem with trying for "correct" prediction is that there are no guarantees your prediction will be correct, often even in simple cases. No model is perfect.