Forecasting Employee Retention Probability Using Back Propagation Neural Network Algorithm

Abstract

The Artificial neural networks are relatively crude electronic networks of "neurons" based on the neural structure of the brain. It process the records one at a time, and "learn" by comparing their prediction of the record with the known actual record. The errors from the initial prediction of the first record is fed back into the network, and used to modify the networks algorithm the second time around and so on for many iterations. The goal is to identify potential employees who are likely to stay with the organization during the next year based on previous year data. Neural networks can help organizations to properly address the issue. To solve this problem a neural network should be trained to perform correct classification between employees. After the network has been properly trained, it can be used to identify employees who intent to leave and take the appropriate measures to retain them