Open Collections

Abstract

An online job listing web site has extensive data that is primarily unstructured text descriptions of the posted jobs. Many listings provide a salary, but as many as half do not. For those listings that do not provide a salary, it is useful to predict a salary based on the description of that job. We tested a variety of regression methods, including maximum-likelihood regression, lasso regression, artificial neural net- works and random forests. We optimized the parameters of each of these methods, validated the performance of each model using cross validation and compared the performance of these methods on a withheld test data set.