I was reading this tutorial about loan prediction: https://rstudio-pubs-static.s3.amazonaws.com/190551_15f6124632824534b7e397ce7ad2f2b8.html
In the 'Preparation' section, the author cuts down the dataset from 111 variables down to 18 by "selecting out irrelevant data, poorly documented data and less important features".
My question is this: is there an efficient way to go through all 118 variables and work out whether they are "irrelevant/unimportant"? I was trying to do it, but there is no way to tell whether a certain factor (eg number of bank accounts that a borrower owns) will be a useful predictive feature.
I have heard of feature selection algorithms, but will these be reliable for cutting 118 features down to 20-30 features? If so, would it be better to trim it down manually first, and then put it through a feature selection algorithm?