How do I select the relevant features of the data? Recently I was working on a problem to do some cost analysis of my expenditure for some particular resource.
I usually make some manual decisions from the analysis and plan accordingly.

I have a big data set in excel format and with hundreds of columns, defining the use of the resource in various time frames and types(other various detailed use).
I also have information about my previous 4 years of data and actual resource usage and cost incurred accordingly.

I was hoping to train a NN to predict my cost beforehand and plan even before I can manually do the cost analysis.

But the biggest problem I'm facing is the need to identify the features for such analysis. I was hoping there is some way to identify the features from the data set.

PS - I have idea about PCA and some other feature set reduction techniques, what I'm looking at is the way to identify them in the first place.
Since you have all your data in a table, a relatively simple thing to do is to consider each column independently, and then seeing if the output variable (cost incurred) has a correlation to that.

If the column has no (or very low correlation) with the output variable, then consider it to be not important.  The ones that make the cut are then considered further.

This is obviously not very different from how a decision tree algorithm would work (such as ID3).