Layoffs Analysis and Prediction Using Machine Learning Algorithms

Abstract

This research aims to develop a model that can predict layoffs within a company using a combination of financial performance, industry trends, and company demographics as input variables. A machine learning algorithm was trained and tested using a dataset of historical data to identify patterns and correlations between these factors and layoffs. The model was then evaluated using a separate dataset to determine its accuracy in predicting future layoffs. The results showed that the model performed well, with high degree of accuracy in predicting layoffs. The research provides valuable insights for companies to better prepare for potential workforce reductions and for employees to anticipate potential job loss. The ability to predict layoffs can also help companies make better decisions on how to manage their workforce and minimize the impact on their employees. This research provides a valuable tool for companies to better plan for future workforce reductions, and for employees to be more aware of potential job loss.