Better predictions of employee events

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.

Intern: 
Nafiseh Sedaghat
Faculty Supervisor: 
Leonid Chindelevitch
Province: 
British Columbia
Partner University: 
Discipline: 
Program: