Machine Learning-based Employee Turnover Prediction

Given the current challenging labor market, many organizations are grappling with significant issues related to employee turnover. This turnover can lead to considerable economic detriments for businesses, rendering the reduction of turnover rates a critical priority. This research leverages an open-source dataset on employee turnover, executing comprehensive data preprocessing and feature engineering. It conducts descriptive statistical analysis and correlation assessments and explores the application of various clustering algorithms such as K-means and DBSCAN to HR data. The study evaluates the effectiveness of these methods through comparative analysis, providing insights into the most suitable clustering techniques for handling HR datasets. The study implements a strategy based on the Random Forest algorithm, coupled with a Grid Search technique, to enhance both the accuracy and generalizability of the classification model. This approach not only boosts the predictive capabilities of the model but also showcases an innovative application of machine learning techniques in model selection and optimization. The findings of this study serve as a valuable resource for corporate management, offering strategic guidance to mitigate unnecessary employee turnover, thereby fostering a more stable workforce environment.