Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm

Abstract

Abstract Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Companies may incur losses due to employee turnover if they are unable to replace lost expertise and productivity. Consequently, this research suggests a new model that uses machine learning to forecast staff turnover. The datasets are collected from Kaggle resource. The dataset has been pre-processed using standard scalar with Label Encoding method. The dataset has been trained with ML algorithm. The best features are selected by using modified genetic algorithm (MGA). The classification has been done with KNN, Gradient Boosting and Extra tree classifier. Finally, the attrition prediction using optimized levy fruit fly optimization (OLFFO). The experimental results are compared with ML algorithms with classification metrics (Accuracy, Precision, recall and f-measure).