This thesis examines how the prediction of voluntary employee turnover could bring value to organisations. A case study was performed with data of Deloitte Holding B.V., consisting of employee records. Four classification models were used as predicting methods. CRISP-DM was used as guiding principles for the application of data mining. The data set was re-sampled as it showed to be imbalanced. Based on F1 score as leading performance measure, it was concluded that Random Forest was the best predicting model for Deloitte. Literature pointed out that voluntary employee turnover was shown to be dysfunctional. Hence, there was concluded that decision trees empowers organisations to identify profiles that form a ‘risk’ for the organisation. Organisations can use decision trees as insights in order to develop effective policies and strategies for retaining employees. However, voluntary employee turnover remains a complex phenomenon, which is only able to explain a small percentage of the variance of the actual turnover decision.
Keywords: voluntary employee turnover, classification, imbalanced data