By: Anjali Sharma, Prabuddha Tamhane, Priyanshi Golatkar, Shreyy Sharma, Vaidehi Shah, and Aditya Kasar
This study aims to predict employee turnover and identify organizational factors contributing to attrition by employing predictive analytics techniques. Employee attrition, which refers to the loss of employees without immediate replacements, leads to reduced overall workforce productivity. The study evaluates different machine learning techniques, such as gradient boosting, random forest, and logistic regression, to identify employees who are most likely to leave, and uses SPSS software to ensure the reliability of the models. Among these methods, gradient boosting produced the most accurate results. The study highlights that factors such as age, income, and work-life balance significantly influence employee turnover. Recruiters can leverage this information to enhance employee satisfaction. The application of advanced techniques, such as neural networks and analysis of employee feedback, has deepened our understanding of employee sentiments. Monitoring these patterns over time will provide valuable insights.
Keywords: Employee attrition, Predictive modeling, Logistic Regression, Random Forest, Data Analysis, Employee turnover, Gradient Boosting, Human Resource Management, Sensitivity, Specificity
Citation:
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