Employee Churn is a common occurrence in the modern business industry. An employee might unexpectedly leave a company or may be let go. This turnover of employees creates a lot of problems as a company invests a lot of valuable time and resources in preparing the employee for working in the company and then monitoring their performance. Employee Attrition also creates an opportunity for the business rivals as they can now hire the rejected employees who know secrets about their companies. This paper focuses on the research on this field and how supervised machine learning techniques have been used to predict the churn rate with maximum accuracy and which method is most suitable for the accurate prediction by comparing their analysis. The prediction of churn rate is based on various employee characteristics which include both technical and personal factors. We have further to tried to use the results of the predictions to come up with techniques on how to reduce the attrition of employees in the future