Using Machine Learning to Predict Employee Resignation in the Swedish Armed Forces

Detta är en Kandidat-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: Since the Swedish government reinstated conscription in 2017, the Swedish Armed Forces are once again able to meet the wartime staffing requirements. In addition to the increase in employees the Swedish Armed Forces have been able to shift focus from external recruiting to internal human resource management. High employee turnover is a costly affair, especially in an organization like this one, where the initial investments, by way of training, are expensive and arduous. Predicting which employees are about to resign can help retain employees and decrease turnover and in turn save resources. With sufficient data, machine learning can be used to predict which employees are about to resign. This study shows that the machine learning model, random forest, can increase accuracy and precision of predictions, and points to variables and behavioral indicators that have been found to have a strong correlation to employee resignation.

  HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)