A Study on Classification of Students Depression Based on Gradient Boosting Algorithm

With the continuous development of artificial intelligence technology, its application has gradually penetrated into the field of psychiatry, especially in the early prediction and auxiliary diagnosis of depression. In this paper, we used the student depression dataset on Kaggle, which contains the relevant information of 27902 students, solved the data imbalance problem through data preprocessing, feature coding and oversampling processing, and constructed various machine learning models such as KNN, SVM, and Gradient Boosting for comparative analysis. The results show that the Gradient Boosting model performs well on the standardized processed data and significantly outperforms other models. This study proposes using the Gradient Boosting algorithm, combined with decision tree techniques, to classify student depression and provide theoretical support for subsequent clinical applications.