Analysis And Prediction Of Enrollment Data Of Private Colleges And Universities

If private colleges and universities want to recruit students who meet the enrollment plan in the college entrance examination every year,they must do a lot of analysis,research and prediction of enrollment data in the early stage,be familiar with the enrollment policy,follow up the enrollment situation,timely grasp the enrollment situation and enrollment rate of each major,and clearly know the distribution of students in each region,so as to obtain a lot of valuable information Interest is convenient for making timely adjustments in the setting and planning of major,enrollment plan,student selection and enrollment publicity in the future,so as to ensure the sustainable and long-term development of private colleges and universities.Therefore,making a reasonable enrollment plan,accurately positioning the target of students,and doing a good job of early enrollment data analysis and prediction are the primary issues to be considered before private colleges and universities enroll students every year.In this study,Apriori algorithm is used for data analysis.Through the analysis of previous enrollment data,valuable and regular knowledge can be found and found.Then,grey prediction model algorithm is used to effectively predict the future enrollment of private colleges and universities,which provides an effective and scientific reference for the recruitment department of Private Colleges and universities in formulating the next year's enrollment plan.First,based on the enrollment data of Minnan University of science and technology from 2010 to 2019 as reference data,the association analysis algorithm is selected for comprehensive analysis to determine some factors that are highly correlated with the registration rate,and on this basis,the association rule model is established and the accuracy of the model is tested.Second,the Grey Prediction GM(1,1)model is used to predict the total enrollment of Minnan University of science and technology in 2020,and it is compared with the prediction data of the second moving average model and the trend extrapolation method.The accuracy of the Grey Prediction GM(1,1)model is tested by the analysis and comparison results,and the prediction of the total enrollment by the Grey Prediction GM(1,1)model is obtained The conclusion of minimum error.In the actual enrollment process,the enrollment plan has volatility,so the established Grey Prediction GM(1,1)model is improved by introducing residual estimation to improve the accuracy of the prediction results.Forecast provincial enrollment plans.