There is a common question type that gets down voted or closed here. Rightly so in my opinion. However, I think the steady stream of similar questions needs somewhere we can point them.
Typical scenario is:
A beginner to machine learning has a data set, imagined or real, and a problem to solve.
As a beginner, they are confused about selecting the "best" modelling approach. There are loads of choices to make after all, and most involve learning about the model class and an API for it in some framework.
The question is presented better than "Here's some data, what do I do?" or "Give me the model?" questions in that the poster is aware of some options in front of them and has a real problem that they are trying to solve themselves. However, despite trying, they have still managed to post a question that is badly framed or too broad.
The question is at a fork in the road - either the poster could share more concrete details about what they are doing, or they could simply be encouraged to try and test their own ideas. Both could be acceptable outcomes, it is not 100% clear whether the problem needs more guidance than "What I'd do is try all 3 of your ideas and test them - so why don't you do the same?".
The reality is that even with a lot of experience, second-guessing the best model for a problem is not how things work. Instead an expert will typically explore the data and problem, select a metric to measure success, then try a range of possible models and feature engineering, testing each.
I think the asker's desire is to get some help through the model selection maze. However, they tend to forget how unique each data problem often is, and/or seem to think that more experienced ML experts will somehow know what to do, sometimes only with the problem domain, a hand-waving description of the data and a rough goal.
I'd like either this question, or even a canonical duplicate, to serve as a pointer to how the posters could improve their questions so that they can be answered and/or to be a frame challenge - i.e. the experts don't just read literature looking for "best solution to problem X", in fact they mostly just try and test stuff; the best thing is to learn the skills that enable the asker to do the same.
Some recent example questions: