FORECASTING EMPLOYEE TURNOVER

Supervised machine learning methods are described, demonstrated and assessed for the prediction of employee turnover within an organization. In our project, numerical experiments for real and simulated human resources datasets representing organizations of small-, medium- and large-sized employee populations are performed using random forest method and logistic regression method .Through a robust and compre- hensive evaluation process, the performance of each of these supervised machine learning methods for predicting employee turnover is analyzed and established using statistical methods. Additionally, reliable guidelines are provided on the selection, use and interpretation of these methods for the analysis of human resources data sets of varying size and complexity.Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. To solve this problem, organizations use machine learning techniques to predict employee turnover. Accurate predictions enable organizations to take action for retention or succession planning of employees. However, the data for this modeling problem comes from HR Information Systems (HRIS); these are typically under-funded compared to the Information Systems of other domains in the organization which are directly related to its priorities. This leads to the prevalence of noise in the data that renders predictive models prone to over-fitting and hence inaccurate The main contribution of our project is to explore the application of Random forest and Logistic Regression technique which is more robust because of its regularization formulation.