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@article{168880,
        author = {ganesh vanve and pragati vanve},
        title = {Employee Promotion Prediction Model Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2251-2257},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168880},
        abstract = {In the service and business sectors there is a constant pressure on employees to be promoted to a higher grade. There is a lot of pressure on the human resource team too for maintaining the employee engagement and motivation, where incentives like promotions and pay are used to control the employee emotions towards work. The promotions are mainly intended to show the appreciation for the employee's contribution towards continual improvement of business standards, maintain the competency between teams, to avoid the talent from leaving and maintain the high standard of performance. Throughout the assessment year human resource collects the large amount of data on all aspect of employee engagement activities. The data gathered are keep growing with employee service and it will be not useful if it does not provide any insights from it. Thus, the machine learning has become the main component in human resource analytics to gather the useful insights from employee data. The problem is with the traditional method of promotion is time and resources consuming because of various steps involved in segregation and promotion procedure. This directly impacted the transition of employs into their new roles. For that reason, it is more efficient if human resource predict which employee is more eligible and suitable for promotion or high grade, salary hike etc. The goal of this study is to propose or predict the employee promotion using machine learning method that could help in predict the which employee could get promoted based on the data collected and past performances. To find out the probability of promotions, various classification algorithms such as decision trees (DT) classifier, logistic regression (LR), random forests, and K-means clustering are the most comprehensive and widely used. The k-nearest neighbors (k-NN), RF, and DT classifier are used for predictions.},
        keywords = {K-NN, decision trees, K-means clustering, human resource analytics, Promotion, etc.},
        month = {October},
        }