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Publicação
Human Resources Analytics for Turnover Prediction: Insights from an Outsourcing Software Company
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.21 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Retaining top talent in companies is critical. This study explores the application of Machine
Learning (ML) techniques to predict employee turnover and identify key turnover motivators
within an IT outsourcing company. Using the CRISP-DM methodology, the study follows a
structured approach to business understanding, data preparation, model training, and
evaluation. A total of ten ML models were developed, with an Artificial Neural Network (ANN)
achieving the highest predictive performance, as measured by F1 scores. Model explainability
was addressed through SHAP (SHapley Additive exPlanations) values, enabling the
identification of the most influential factors in predicting turnover. The results reveal that
project duration, absence days, time to change contract terms, and commissions were among
the most significant predictors of employee turnover. All models demonstrated strong
performance, highlighting the feasibility of using ML in Human Resource Analytics (HRA) for
this purpose. This study provides insights for companies seeking to proactively manage
turnover by highlighting the key drivers influencing employees’ decisions to leave.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Human Resource Analytics People Analytics Employee Turnover CRISP-DM Predictive Analytics SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure