Employee Attrition System Using Tree Based Ensemble Method

Employee attrition has become a vital problem across the world. It is one of the crucial issues faced by business leaders within companies where they lose the most talented employees. A good employee is always an asset to the organization and their resignation can lead to various problems like financial losses, overall performance, and loss of acquired knowledge. Furthermore, hiring new employees is far exorbitant, taxing, and time-consuming in comparison to recruiting the existing one. It is very time-consuming to recruit a new employee as it takes him months for training, adjusting to the culture, rules, and environment. Therefore, upcoming trends and technology using Machine Learning Algorithms must be exploited for the benefit of business organizations. Knowing the reason beforehand for the employee attrition, companies can mitigate this loss. This paper provides a conclusive review of employee attrition using the tree-based Ensemble Machine Learning Model from the dataset ‘IBM HR Analytics Employee Attrition Performance’.A collection of statistically significant factors which connect to an employee's decision to leave are identified. The paper evaluates the tree-based ensemble to get the best results from the existing tree methods.