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Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering

Annals of Computer Science and Information Systems, Volume 38

Machine Learning Approach for Forecasting Job Appeasement and Employee Corrosion

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DOI: http://dx.doi.org/10.15439/2023R17

Citation: Proceedings of the 2023 Eighth International Conference on Research in Intelligent Computing in Engineering, Pradeep Kumar, Manuel Cardona, Vijender Kumar Solanki, Tran Duc Tan, Abdul Wahid (eds). ACSIS, Vol. 38, pages 85–91 ()

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Abstract. Employee turnover imposes costs on the organization. The quit may also cause significant and costly disruptions to the production process. The recent increase in the technological capacity to gather large magnitude of data and analyze it has changed how decision makers use them to decide on making the optimal decision. Employee attrition very similar to customer churn is an important and deciding factor affecting the revenue and success of the company. To avoid this problem, many companies now are taking guide via machine learning strategies to expect employee churn/attrition. In this paper, we are analyzing data from the past and present using different classifications like SVM, Random Forest, Decision tree, Logistic Regression, and an Ensemble model to come up with a better predictive model for the present dataset. Through this we are hoping to help the company predict employee churn and take effective measures to retain the employees and improve their economic loss due to the loss of valuable employees.

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