Application of artificial intelligence to predict employee turnover in a supermarket in Juliaca – Puno
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Abstract
This study focused on the development and evaluation of an artificial intelligence model for predicting employee turnover in the fresh produce section of a supermarket located in Juliaca, Peru, and on identifying the main determinants of this phenomenon. A quantitative and predictive methodological approach was adopted, employing machine learning algorithms (Logistic Regression, Random Forest, XGBoost, KNN) on a database composed of 1,000 employee records. The methodology included data preprocessing, variable encoding, feature scaling, and the implementation of the SMOTE technique to counteract class imbalance. The findings revealed that the KNN model exhibited the superior predictive performance, achieving an F1-Score of 0.599. Feature importance analysis indicated that monthly salary, performance appraisal, and seniority were the most influential predictors. In conclusion, it is established that artificial intelligence represents a robust and feasible tool for proactive human resource management, empowering management to formulate focused and evidence-based retention strategies to mitigate turnover in the analyzed context.
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