Predicting the Number of College Students Taking the Postgraduate Entrance Examination Based on Machine Learning Algorithm

With the increasing employment pressure and people's urgent need to improve their academic qualifications, the number of postgraduate entrance examinations is increasing year by year, and more and more candidates are participating in the postgraduate entrance examination queue. In order to help analyze and predict the number of postgraduate entrance examinations, this paper hereby proposes a scheme based on machine learning algorithms to predict the number of postgraduate entrance examinations. First, the collected public data for postgraduate entrance examinations are visualized and analyzed; then AdaBoost regression in the field of machine learning is selected (single model uses k-Nearest Neighbor Regression, Limit Tree Regression, Random Forest Regression, Linear Regression, Decision Tree Regression) algorithm modeling; finally we use the model built separately to predict the new incoming data. The entire model training and prediction process is simple, and with the help of artificial intelligence and machine learning algorithm models, it can help realize intelligent analysis and prediction of the number of graduate students.