National Labour Office – Profiling the unemployed with machine learning
For the National Labor Office, we used a machine learning approach to analyze anonymous personal records for the Hungarian unemployed in order to discover how demographic features characterize chances for people staying unemployed, obtaining a job or retiring.
The demographic traits for
- the geographical location (NUTS Level 3)
- age group
- the level of education
were used to study how long an individual stayed in the unemployment support system and what chances the beneficiary had for a particular employment status when he or she left the benefit system.
Having performed Principal Component Analysis in the space of demographic features and unemployment status, we used a random forest meta estimator to fit a number of randomized decision trees on various sub-samples of the dataset. A linear combination of age and education level turned out to be the main driving factor for the length of various employment statuses.