High-Tech Industry Employees Turnover Intention Prediction Model Based on Machine Learning

This article uses machine learning methods to establish the prediction model of the high-tech industry employee turnover tendency. Firstly, ridge regression analysis and the XGBoost algorithm are used to explore the factors affecting the turnover intention of high-tech industry employees. Then, a predictive model was established using ridge regression, and the XGBoost algorithm was used to predict employee turnover trends in the high-tech industry on the test set. Through ridge regression analysis and the XGBoost algorithm, it is found that the most important factor affecting high-tech industry employees' turnover intention is workplace interpersonal relationships. Therefore, the first step in reducing the willingness of the high-tech industry employee turnover intention is to strengthen employees' interpersonal relationships in the workplace. The prediction evaluation results of the XGBoost algorithm on high-tech industry employee turnover tendency indicate that the algorithm has good prediction ability, small prediction error, and strong data interpretation ability.