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Home / Archives / Volume-6 / Issue-4 / Article-1

Volume - 6 | Issue - 4 | december 2024

Employee Promotion Evaluation and Prediction using Machine Learning Open Access
Nareen Ansari  , Neha Vora  246
Pages: 317-332
Cite this article
Ansari, Nareen, and Neha Vora. "Employee Promotion Evaluation and Prediction using Machine Learning." Journal of Information Technology and Digital World 6, no. 4 (2024): 317-332
Published
17 October, 2024
Abstract

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.

Keywords

Employee Promotion Prediction Promoted Machine learning Algorithms Promotion Evaluation

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