IJAIEM

T.Sandeep Reddy , B.Vijay Laxman , MD.Faisal, K.Yadagiri

Abstract

Predicting employee performance is essential for organizations. The success or failure of a company often depends upon the competence of its employees, so CEO’ s and managers who want their organizations to succeed face the difficult task of determining which employees should be promoted. The current promotion process used in most organizations should be considered misleading because it depends on supervisors judgments. The major aim of this paper is to use classification algorithms to develop predictive models for predicting whether an employee is qualified for promotion or not and identifying the most important attributes affecting employee promotion. The data set used in this paper is from Kaggle 2020. It contains information on multinational companies arranged in 54,808 rows and 13 columns. This data set covers nine broad verticals across organizations. Several predictive modeling techniques,including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, Su