EFFICIENCY AND PREDICTION IN HUMAN RESOURCE MANAGEMENT USING PYTHON MODULES

Mihai Androniceanu ()
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Mihai Androniceanu: Ministry of Transport and Infrastructure, Romanian Railway Authority, Bucharest, Romania

Theoretical and Empirical Researches in Urban Management, 2025, vol. 20, issue 1, 88-103

Abstract: Python has become an increasingly popular platform due to its ability to automate processes, analyze data, and improve the efficiency of human resources management (HR) operations. Python can assist in enhancing recruitment processes, employee performance evaluation, employee satisfaction analysis, and more. Python is a highly popular programming language, known for its simple syntax and versatility, which is why it was chosen for this research. The research aimed to provide HR managers with an analysis model for organizational employee mobility. The specific objectives were: (1) identifying the correlations between research variables and their impact on employee mobility; (2) identifying the main causes of employee mobility; (3) ranking the variables with the most impact on human resources fluctuation; (4) developing predictions regarding the stability of human resources in positions and functions within an organization. The research is a pilot study conducted within an organization using Python modules based on data from the year 2024. The research results show the significant influence on employee mobility exerted by factors such as the level of education, seniority, and age of employees within the sample. Another useful outcome for HR managers is the predictive model obtained with the help of Python modules, which allows them to both analyze and predict the profile of employees with a higher degree of stability within the organization. The research demonstrates how various artificial intelligence applications can be integrated into Python-specific modules to enhance human resource management and organizational efficiency.

Keywords: human resources management; Python modules; stability of the human resources; mobility, fluctuation, artificial intelligence. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:rom:terumm:v:20:y:2025:i:1:p:88-103

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