You need funding, you need a better algorithm, a better implementation, or you need to change your problem. To get funding, you need to work with your adviser. This could be about using grant money or getting a grant for an XSEDE allocation. If your adviser has neither of those, it may not be a good choice to work with them even if you like their research: science requires funding.
If you want to make it cheaper, then think about a different algorithm. Even if it's doing a lot of branching and stuff like that, there must be some way to utilize linear algebra operations in some parts to make use of GPU (or Xeon Phi) acceleration. Maybe you need to precompute some values, or need a wholly different approach. Only you (and your adviser) would know. In fact, this algorithm development is many times the entire purpose of the research. Your adviser may have given you this project because no simple computer can solve this problem. Your goal isn't to find a bigger computer, it's to find out how to make your computer solve this problem (or at least, find out how to solve it better). No online tutorial or anything can help you with this: it's real research.
You also need to take a look at your implementation. If you're looping and branching in a high level language like Python, this could be the problem. You either need to re-write the code to be vectorized to do a bit better, or to get "production-quality" speeds you need to re-write it in a non-allocating format in a performance language (one which generate strictly typed and interprocedurally optimized compiled code) like C++ or Julia. It's not hard to find non-vectorized Python codes which can be made 1000x faster just by doing a better implementation and being careful with memory use (even vectorization is bad with memory use so that still leaves a lot to optimize).
And lastly, if this is a highly researched area where it's well-known it cannot do any better and you cannot get the funds/allocations to run the algorithms, pick a different project. Sometimes problems currently aren't possible. I highly doubt that your adviser would give you a problem that is in this domain though given that you're an undergraduate.
However, given you're a younger undergraduate, I would double/triple/quadruple check that you are actually correct that this kind of computing power is even required before going to blame anyone else. Embarrassingly parallel and (100 individuals * 116,323 records) is quite small, even to train on a desktop computer without a GPU. You should really make sure everything is as fast as it can be (rewrite it a few times in a few different languages, benchmark and profile each part, get rid of all memory allocations in the inner loops, etc.), make sure there's no better algorithm choices, make sure there is no cluster allocation you can get on, and only then should you consider moving to a new project that is more suitable to your current access to computing power.