Employee Promotion Evaluation and Prediction using Machine Learning

Promoting an employee is an important responsibility of the HR department. Various factors contribute to an employee's promotion, such as age, recruitment channel, number of training, academic qualifications, and length of service for the employee. These factors majorly affect the promotion. This research explores employee promotion evaluation and aims to predict whether an employee will be promoted. The dataset used is a primary dataset, which has been gathered through surveys from employees asking for their information. In this study, predictive analysis will be studied based on the criteria estimated for the employees in the promotion process by machine learning algorithms such as logistic regression, random forest classifier, gradient boosting classifier, and decision tree classifier. Logistic regression achieved the highest performance with 86% accuracy and 86% precision. This research can be beneficial to HR and managers in evaluating and predicting employee promotions.