Prediction of Employee Turn Over Using Random Forest Classifier with Intensive Optimized Pca Algorithm

Abstract

Employee turnover is the important issue in the recent day organizations. In this paper, a data mining based employee turnover predictor is developed in which ORACLE ERP dataset was used for sample training to predict the employee turnover with much higher accuracy. This paper deploys impactful algorithms and methodologies for the accurate prediction employee turnover taking place in any organization. First of all preprocessing is done as a precautionary step as always before proceeding with the core part of the proposed work. New Intensive Optimized PCA-Principal Component Analysis is used for feature selection and RFC-Random Forest Classifier is used for the classification purposes to classify accordingly to make the prediction more feasible. For classifying and predicting accurately, a methodology called Random Forest Classifier (RFC) classifier is deployed. The main objective of this work is to utilize Random Forest Classification methodology to break down fundamental purposes lying behind the worker turnover by making use of the information mining technique refer as Intensive Optimized PCA for feature selection. Comparative study taking the proposed novel work with the existing is made for showing the efficiency of this work. The performance of this proposed method was found to perform better with improved yields of ROC, accuracy, precision, recall, and F1 score when compared to other existing methodologies.