Employee attrition prediction is a crucial aspect of workforce management, especially for organizations aiming to optimize their retention strategies and maintain operational stability. Through the utilization of advanced data analytics and machine learning algorithms, this internship project aims to develop a predictive model capable of forecasting employee turnover. By leveraging historical data encompassing various factors such as job satisfaction, tenure, performance metrics, and demographics, the model endeavors to identify patterns and indicators that precede attrition events. Ultimately, the goal is to provide actionable insights to HR departments, enabling proactive measures to mitigate attrition risks, enhance employee satisfaction, and foster a more stable and productive workforce environment. This project not only offers valuable practical implications for businesses but also provides an opportunity to delve into the realms of predictive analytics and machine learning in the context of human resource management.
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