Despite rising unemployment, most job coverage of the COVID-19 outbreak has concentrated on layoffs. Employees have been fired for reasons related to the epidemic, which has been a less prominent issue. COVID-19 is still doing damage to the country's economy. Companies are in the midst of a recession, so they are beginning to fire off unproductive employees. Making critical decisions like laying off employees or cutting an employee's compensation is a challenging undertaking that must be done with extreme attention and accuracy. Adding negligence would harm the employee's career and the company's image in the industry. In this paper, we have predicted employee attrition using Logistic Regression, Random Forest, and Decision Tree techniques. Random Forest Classifier has outperformed other algorithms in this work. After using different machine learning techniques, we can say that Random Forest gives the best performance with a recall of 70%, and also, we have found Precision, Accuracy, and F1- Score.
Nayak, Soumen and Palai, Pranati
"Employee Attrition System Prediction using Random Forest Classifier,"
International Journal of Computer and Communication Technology: Vol. 9:
1, Article 8.
Available at: https://www.interscience.in/ijcct/vol9/iss1/8