lasso is probably the easiest way to do it relatively quickly.
Lasso is also known as L1 regularisation, and it tends to set the coefficients to a bunch of features to zero, hence performing feature selection.
Note that if two predictors are very correlated, lasso may pick one mostly at random. Obviously one should do CV and bootstrapping to ensure that the results are relatively stable.
In general though, there's no real substitute for domain expertise when it comes to selecting good features.
Lasso is also known as L1 regularisation, and it tends to set the coefficients to a bunch of features to zero, hence performing feature selection.
Note that if two predictors are very correlated, lasso may pick one mostly at random. Obviously one should do CV and bootstrapping to ensure that the results are relatively stable.
In general though, there's no real substitute for domain expertise when it comes to selecting good features.
edit: lasso is L1, not L2