Abstract:
This master thesis deals with the prediction and interpretation of voluntary employee turnover
using machine learning and relates those findings to a cost reduction. In order to provide a theoret ical basis, different employee turnover costing frameworks, machine learning methods and feature
importances from literature are evaluated. Based on these findings, insights into current turnover
and retention costs for the company are obtained. Furthermore, historic employee turnover data
is used to train and optimize six state-of-the-art machine learning models, which classify employ ees who are at risk of leaving. Ultimately, an optimal version of the best performing model is
proposed and related to tangible insights. Individual classifications of this model are interpreted
so that the insights can be used for global and individual retention strategies. A novel technique
named SHAP is used to for this interpretation. Additionally, guidelines are provided on how to
obtain these insights in novel situations when the model is used in practice. Lastly, the final
model performance and interpretations are related to the turnover and retention costs, providing
an indication of a possible cost reduction that can be achieved.
