Determining the intention of new recruits to quit or stay in the organisation: An empirical study on private sector bank employees of India

The article is aimed to identify the candidates whose intention to leave is strong in near future thus trying to bridge the gap between the research and practical application in the problem area of private banks of India. The candidates that are spoken about are those who would be selected afresh to join the organization. Here, a framework is defined to predict the candidates who would be retainable by the banks. By predicting the intention to leave the organization, the organization would be able to plan the turnover and reduce the cost to the organization to make them profitable. The article uses three supervised machine learning techniques to undertake the prediction of candidates by classifying the dataset into two classes i.e. those who as “intention to quit” and “intention to stay”. The three algorithms are Logistic regression, Support Vector Machine and Random Forest Algorithm, Logistic regression giving the highest accuracy of 88.9%.