Publicação
Employee Turnover Predictive Model: A model to predict who is more likely to leave a company
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Henriques, Roberto André Pereira | |
| dc.contributor.author | Durães, Nuno Rafael de Almeida | |
| dc.date.accessioned | 2024-11-15T15:05:54Z | |
| dc.date.available | 2024-11-15T15:05:54Z | |
| dc.date.issued | 2024-11-06 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.abstract | This master's thesis explores the predictive modeling of employee turnover within a company in the Retail & Food Industry, using a data-driven approach to identify potential leavers and understand the dynamics affecting their decisions. Employing machine learning techniques such as Logistic Regression, Random Forest, and Neural Networks, the study focuses on optimizing the prediction of employee turnover through sophisticated model selection and hyperparameter tuning. The research began with an extensive data preparation phase, which involved cleaning, normalization, and transformation of the dataset to ensure robustness and relevancy for model training. This process also included the application of SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance within the dataset, ensuring that the predictive performance was not biased towards the majority class. Key features influencing turnover, such as job satisfaction, management styles, compensation packages, and career progression opportunities, were identified and engineered to enhance the predictive performance of the models. Several models were evaluated, with the ensemble approach integrating Random Forest, Gradient Boosting, and Neural Networks showing the most promising results. This ensemble model, optimized for high precision in predicting 'Active' status without overfitting, achieved remarkable accuracy (95.1% to 95.8%), precision for label 0 (non-leavers) up to 92.5%, and an ROC-AUC score demonstrating excellent classification capabilities (up to 0.983). The refined models significantly outperformed initial predictions, highlighting the effectiveness of the feature selection and machine learning techniques employed. The findings suggest that the integrated approach can effectively predict employee turnover, providing HR departments with a valuable tool for strategic human resource planning. This predictive capability enables proactive interventions tailored to mitigate turnover and enhance employee retention strategies. In conclusion, this thesis not only demonstrates the applicability of advanced analytical techniques to real-world HR challenges but also lays the groundwork for future research. It suggests exploring further cross-industry applications, integration of additional data sources, and employing alternative modeling techniques to expand the model's robustness and adaptability across different organizational contexts. | pt_PT |
| dc.identifier.tid | 203777859 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/175319 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Employee Termination | pt_PT |
| dc.subject | Employee Turnover | pt_PT |
| dc.subject | Predictive Modelling | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Human Resources Analytics | pt_PT |
| dc.subject | Employee Retention | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.title | Employee Turnover Predictive Model: A model to predict who is more likely to leave a company | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio | pt_PT |