Practically speaking, you may be sufficiently well equipped for performing the research and writing it up. But, as others have mentioned, you almost certainly lack experience and understanding of the field to explain why you did it and what impact your work has.
And most experienced researchers are able to spot some common issues in the presentation, even in topics they do not know much about.
- You can publish, and I would absolutely encourage you to try. Mighty oaks from little acorns grow.
- It is a lot easier for a student to publish a research paper solo rather than to publish a literature review, although the latter is also commonly done - as a part of a thesis project and under supervision.
- One potential issue might be presentation. You should enlist some help to review the article before sending it to a journal.
- Another could be a flawed experimental design (if it is experimental research we are concerned with). This is what training is for, and it is hard: very few can get it right, much less first try. Do not let that discourage you, however: this is where practice and feedback are especially helpful. More on that point later.
- Yet another issue does not have to do with research but with the submission process. From personal experience and communications, it could be an unexpectedly enormous roadblock. Suppose you read enough literature, and you also got an interesting result and wrote a paper about it, and this paper looks no worse than what you see out there... Now what? Teaching you the process of picking a journal, submitting a publication, and interacting with its editorial office and reviewers is yet another role typically assumed by an advisor. You may or may not find this problematic, but if you end up struggling, again, enlist help.
Now, to expand on the experiment design... One piece of advice I could give you is this: before doing anything, pretend for a moment your experiments showed very favorable results, the best you could plausibly imagine. Try to describe them; do they make for a convincing case? If not, this is a bad, bad design; figure out what is missing. Do not underestimate this pitfall, it is far more common than you think.
Then, consider what happens if the results were very weak, sitting firmly at the previously established baseline. No improvement at all. Could you extract any knowledge from that? If not, this is still fine, but consideration should be given, especially as the experiments get more and more laborious.