Impact of Work Life Balance on Employee Turnover using Machine Learning Techniques

Abstract

ABSTRACT Employee turnover is one of the most significant problems. The solution of this problem is that organizations are heavily investing their time and cost into machine learning techniques in order to predict the employee’s turnover. This study was conducted to establish an impact of employee’s work life balance on employee turnover. With the advancement in technology, several machine learning algorithms are there along with the respective platform tools available in the market. We used the WEKA Machine Learning Tool for our project. In this project we are understanding which variables are most influential in predicting employee turnover. Job satisfaction is our dependent variable and several independent variables are also chosen. The sample of our study was 124 in total. Most of the respondents were from the IT industry. Primary data was collected through the structural questionnaires. The research findings say that most of the IT industries have adopted several work life balance practices in their organizations. These practices include flexible working hours, childcare facilities, casual leave facility , rotational shifts, work from home facility . It was revealed that a strong relationship occurs between work life balance practices relating to Work from home facility and turnover intent respectively.