Date of Publication :15th December 2024

Abstract: In an era where workforce turnover poses a significant challenge for organizations, our Python-based project addresses workforce turnover by developing an employee churn prediction system. Utilizing historical employee data, including job satisfaction and performance metrics, we employ Python's data analysis and machine learning capabilities for model construction. Through rigorous testing, our project identifies the most effective predictive model for employee churn, providing valuable i nsights to enhance workforce management strategies. Aimed at reducing churn and improving stability, this data-driven solution offers organizations a tool to revolutionize human resources practices, fostering increased employee satisfaction and loyalty.

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