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Within the carrier and business industries, there would be an ongoing demand for employees who are promoted to higher positions in the service and corporate sectors. The human resource team faces significant pressure to maintain employee commitment and motivation. Incentives such as promotions, bonuses, and wages are applied to motivate employees to feel closer to their work. The employee promotions are primarily deliberate, expressing gratitude for the employee’s dedication to enhancing business standards, ensuring team competency, preventing talent from seeking other opportunities, and upholding the excessive degree of overall performance, all through the assessment year, human resources gather a significant quantity of facts on all elements of worker engagement events and activities. The data collected is continuously expanding in terms of employee service, but it is of little value if it does not provide meaningful insights. As a result, machine learning plays a crucial role in human resource analytics by extracting valuable information from collaborative employee data. The issue lies in the conventional approach to promotion, which is both time- and resource-intensive due to the numerous steps required for segregating and promoting employees. This had a significant impact on the smooth transition of employees into their new positions. Because of this reason, it’s miles greater sensible if human assets can predict which workers are more legal and appropriate for advancement or upgrade, earnings increase, and so on. This research aims to propose or expect worker promotion. Utilizing machine learning techniques to forecast which employee might be eligible for a promotion, contingent on the data gathered and their previous achievements. To determine the likelihood of advancement probabilities the classification algorithms together with decision trees (DT), logistic regression (LR), random forests (RF), and k-means clustering are considered broadly utilized within the field. The k-nearest neighbors (K-NN), random forest (RF), and decision tree (DT) classifiers are applied to make the expected forecast.

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Employee Promotion Prediction Model Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • M. R. Bendre,
  • V. P. Vikhe,
  • G. B. Vanve

摘要

Within the carrier and business industries, there would be an ongoing demand for employees who are promoted to higher positions in the service and corporate sectors. The human resource team faces significant pressure to maintain employee commitment and motivation. Incentives such as promotions, bonuses, and wages are applied to motivate employees to feel closer to their work. The employee promotions are primarily deliberate, expressing gratitude for the employee’s dedication to enhancing business standards, ensuring team competency, preventing talent from seeking other opportunities, and upholding the excessive degree of overall performance, all through the assessment year, human resources gather a significant quantity of facts on all elements of worker engagement events and activities. The data collected is continuously expanding in terms of employee service, but it is of little value if it does not provide meaningful insights. As a result, machine learning plays a crucial role in human resource analytics by extracting valuable information from collaborative employee data. The issue lies in the conventional approach to promotion, which is both time- and resource-intensive due to the numerous steps required for segregating and promoting employees. This had a significant impact on the smooth transition of employees into their new positions. Because of this reason, it’s miles greater sensible if human assets can predict which workers are more legal and appropriate for advancement or upgrade, earnings increase, and so on. This research aims to propose or expect worker promotion. Utilizing machine learning techniques to forecast which employee might be eligible for a promotion, contingent on the data gathered and their previous achievements. To determine the likelihood of advancement probabilities the classification algorithms together with decision trees (DT), logistic regression (LR), random forests (RF), and k-means clustering are considered broadly utilized within the field. The k-nearest neighbors (K-NN), random forest (RF), and decision tree (DT) classifiers are applied to make the expected forecast.