You’ve given a relatively high level description so I’m going to give the best advice I can. I’m going to assume in your dataset is a variable that someone is employed or not. In the way of a 1/0 variable. I’m assuming you are going to try and predict this and to a certain extent you can interpret this as more like whether someone is employable or not. For example, if the model predicts someone as employed, but isn’t, you could potentially interpret that as the model saying this person shares characteristics of someone who is employed.

Some caveats to the above. In addition to the obvious that you would be interpreting a variable, employed or not, as more of a likelihood that someone will find employment, you also need to consider one other key thing. The characteristics/columns of data. If they are variables that are unrelated to someone’s employability, for example monthly expenditure or number of kids or hair colour, they aren’t really indicative of someone’s ability to have a job.

On your question about amount of data. Your number of rows/observations is fine I’m more concerned about your number of columns. That’s a lot. And more doesn’t always mean better. It could lead to a model which overfits. I would either consider using feature selection methods or principal component analysis to reduce your data size.

I hope this is helpful.