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

Abstract

Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Employee attrition may be costly for businesses since it takes a lot to compensate for their experience and efficiency. As a result, this study proposes a novel model for predicting employee attrition utilizing machine learning techniques. 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).