Let's Predict Who Will Move to a New Job

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...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Gahar, Rania Mkhinini, Hidri, Adel, Minyar Sassi Hidri
Format: Article
Language:English
Subjects:
Algorithms
Decision trees
Machine learning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2309.08333