Let's Predict Who Will Move to a New Job

TL;DR

This paper explores machine learning techniques to predict employee turnover, utilizing data preprocessing, various ML algorithms, and oversampling to enhance prediction accuracy for HR decision-making.

Contribution

It introduces a comprehensive ML-based approach for predicting employee job changes, including data handling, multiple algorithms, and performance optimization methods.

Findings

Random Forest achieved the highest accuracy.

SMOTE improved model performance on imbalanced data.

Models showed promising precision and recall metrics.

Abstract

Any company's human resources department faces the challenge of predicting whether an applicant will search for a new job or stay with the company. In this paper, we discuss how machine learning (ML) is used to predict who will move to a new job. First, the data is pre-processed into a suitable format for ML models. To deal with categorical features, data encoding is applied and several MLA (ML Algorithms) are performed including Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), and eXtreme Gradient Boosting (XGBoost). To improve the performance of ML models, the synthetic minority oversampling technique (SMOTE) is used to retain them. Models are assessed using decision support metrics such as precision, recall, F1-Score, and accuracy.