Evaluating Machine Learning Algorithms to Detect Employees' Attrition

Employees' Attrition has become a well-known concept among organizations in the recent years, and it is a problem that leads to many issues within organizations. Companies can use the enormous amount of data they have about their employees to help solve this problem. This paper focuses on examining the efficiency of different machine-learning techniques that can be used for predicting employees' attrition. The algorithms were chosen based on a detailed comparison between all Machine Learning algorithms provided in MATLAB which led to choosing the most suitable ones to later identify the top performing among them. The most suitable algorithms found are Logistic Regression, Support Vector Machine (Linear, Quadratic, and Kernel), and Boosted Trees. This paper's findings could make a notable impact on businesses' ability to prevent employee attrition.