I have a large dataset of 30,000 cases with 150 variables. I am looking for a few possible machine learning solutions/methods that I could try and use for cross validation.
My dependent variable is a percent/continuous variable while all my independent variables are continuous or discrete categorical variables. I am only looking for one output which would be the prediction variable which provides a percent (continuous between 0 and 1).
Currently I have run linear, logit and probit models, with probit showing the most promise.
I believe Naïve Bayes is a simple one yet to try, but wondering what other types of machine learning models are out there that would give the desired output.
Update: I am modeling Election Turnout based on past turnout within an individual precinct. One precinct may have 33.33% turnout while another might have 57.65% turnout. The possible outputs could be any continuous percent between 0 and 1.
Any thoughts would be much appreciated! Thanks in advance!