Employee Attrition: Analysis of Data Driven Models

Abstract

Companies constantly strive to retain their professional employees to minimize the expenses associated with recruiting and training new staff members. Accurately anticipating whether a particular employee is likely to leave or remain with the company can empower the organization to take proactive measures. Unlike physical systems, human resource challenges cannot be encapsulated by precise scientific or analytical formulas. Consequently, machine learning techniques emerge as the most effective tools for addressing this objective. In this paper, we present a comprehensive approach for predicting employee attrition using machine learning, ensemble techniques, and deep learning, applied to the IBM Watson dataset. We employed a diverse set of classifiers, including Logistic regression classifier, K-nearest neighbour (KNN), Decision Tree, Naïve Bayes, Gradient boosting, AdaBoost, Random Forest, Stacking, XG Boost, “FNN (Feedforward Neural Network)”, and “CNN (Convolutional Neural Network)” on the dataset. Our most successful model, which harnesses a deep learning technique known as FNN, achieved superior predictive performance with highest Accuracy, recall and F1-score of 97.5%, 83.93% and 91.26%.