Improving employee retention by predicting employee attrition using machine learning techniques
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Salunkhe, Tanmay Prakash
MSc in Information Systems with Computing
Dublin Business School
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This Dissertation aims to help the HR and Project Managers in improving the retention rate of the valuable employees in an organization, thereby minimizing the employee turnover cost of the company. The research was carried out in three stages. To improve the retention rate, efforts were made to first, predict the employee attrition; secondly, decide on which employees are valuable and their retention is profitable to the company. Finally, the factors that influence the employee’s intention to resign from a company is found out and provided to the HR and Project managers through the HR Analytical application developed using R and Shiny R framework. Good amount of research has been done while considering the factors for the employee attrition prediction. Also, the survey for these factors has also been carried out among the HR professionals in my network. Various analysis has been done while selecting the Machine Learning algorithm for training the predictive model. Logistic Regression algorithm is used for building attrition prediction model as it gives the most accurate result. Then, after doing considerable analysis on how to choose the valuable employee and applying methodological assumptions, Decision Model was prepared with the help of conditional logic statements which showcased which employees are valuable and which employees are not. Then the separate employee source file was prepared consisting of the valuable employees and who were also a potential candidate for resignation. Using the R condition statements, the dashboard was developed which shows all the factors influencing employee attrition so that HR and Project managers can use them accordingly retaining valuable employee. For developing and testing the application IBM Employee dataset was used (IBM , 2018). This application can be used by the HR Managers to simplify the employee retention decision.