Workforce Turnover Prediction Using Machine Learning Algorithms.

EasyChair Preprint no. 10974

Abstract

In the age of big data, significant changes are occurring in corporate management. Big data has an impact on human resource management since it is such an essential aspect of the business. The majority of workers quit their existing organizations to gain new skills and expand their competencies. Companies launched various training and development programs in response to this trend, aiming to encourage and thereby keep them. This study uses the working data of business workers as the primary data and analyses it to determine what variables influence worker resignation, the major reasons for resignation, and forecasts of which exceptional people will depart. This project provides a scientific foundation for the accurate management of firms and organizations by predicting indicators using currently popular algorithms (logistic regression, K- nearest neighbor). This project's contents include: analyzing the factors causing worker turnover and mining the influence degree of relevant factors; developing a model using an algorithm and determining the best model parameters to predict which workers will leave; and selecting the best model and parameters through model comparison. We can discover the elements influencing worker turnover through data mining of departed personnel and draw conclusions based on the study results and the real circumstances of the firm.

Keyphrases: Big Data, KNN (K-Nearest Neighbor), logistic regression, machine learning, Workforce

@Booklet{EasyChair:10974,
  author = {Hemanth Kumar Somarathi and Narasimha Reddy Soora},
  title = {Workforce Turnover Prediction Using Machine Learning Algorithms.},
  howpublished = {EasyChair Preprint no. 10974},

  year = {EasyChair, 2023}}