Let's Predict Who Will Move to a New Job
This work addresses the challenge of employee retention for companies, but it is incremental as it uses standard methods without introducing new paradigms.
The paper tackles the problem of predicting employee turnover by applying machine learning algorithms to HR data, achieving performance improvements through techniques like SMOTE and evaluating models with metrics such as F1-Score and accuracy.
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.