JAZ 2023
DOI: 10.53555/jaz.v44is8.3526
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Predicting HR Churn with Python and Machine Learning

J. K. Patil,
P. M. Jadhav

Abstract: Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity los… Show more

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