부산대학교 도서관

Businesses may now effectively understand and lower employee turnover with the use of analytics for human resources (HR). With an emphasis on women's attrition trends, this research investigates the topic of private education institutions. Specifically, gender-specific dynamics in the workplace are emphasised in the first portion of the research, which examines the factors that cause women to leave these institutions. This is followed by the development of a comprehensive evaluation system that combines statistical analysis, machine learning algorithms, and historical data to estimate and understand the attrition patterns. Based on the investigation's findings, female employee turnover at private education institutes may be significantly predicted by a variety of criteria, including work-life balance, opportunities for professional progress, and workplace culture. Sophisticated statistical models, including logistic regression and decision tree analysis, are used in the study to estimate attrition trends and show these variables' relative relevance. Through the implementation of HR analytics, organisations may enhance their ability to predict these concerns, formulate policies that consider gender parity, and foster a fairer and more supportive workplace culture. Apart from emphasising the importance of data-driven decision-making in retaining talent, this study provides HR professionals and organisational leaders with a valuable framework to enhance employee contentment and reduce attrition rates among female students enrolled in private Universities.