  Employee's knowledge is an organization asset. Turnover may impose apparent
and hidden costs and irreparable damages. To overcome and mitigate this risk,
employee's condition should be monitored. Due to high complexity of analyzing
well-being features, employee's turnover predicting can be delegated to machine
learning techniques. In this paper, we discuss employee's attrition rate. Three
different supervised learning algorithms comprising AdaBoost, SVM and
RandomForest are used to benchmark employee attrition accuracy. Attained models
can help out at establishing predictive analytics.
