A Student Employment Prediction Model Based on Enhanced Random Forest and Deep Learning
In order to better equip educational institutions and students to navigate the dynamic job market and enhance the alignment between education and employment,a blended student employment prediction model,combining the im-proved Random Forest algorithm(RF)and Deep Belief Network(DBN),is proposed.Firstly,student data undergo preprocessing to eliminate irrelevant attributes,ensuring data consistency.Subsequently,to enhance the accuracy of the prediction model,a feature selection model incorporating Principal Component Analysis(PCA)and the RF algorithm is employed to choose an optimal subset from the original features.The selected features are then utilized as inputs for the DBN,facilitating the learning of advanced feature representations and facilitating student employment prediction.Experi-mental results demonstrate that the proposed blended model achieves a prediction accuracy exceeding 92%,outperfor-ming Recurrent Neural Network(RNN)and Multilayer Perceptron(MLP)-based models by 16.8%and 14%,re-spectively.The proposed model proves instrumental in assisting educational institutions in understanding employment trends across various majors and courses,thereby improving students'career development planning.
NETL
NSTL
万方数据
