Analysis of Education Data in Vietnam (2002-2023) and Student Enrollment Projections to 2030

In response to the need for the digitalization of educational management processes, this paper addresses the analysis of education data. Based on a dataset covering student enrollment in primary, lower secondary, and upper secondary schools across provinces and cities in Vietnam from 2002 to 2023, the study employs descriptive, diagnostic, and predictive analytical approaches. For predictive analysis, the paper utilizes linear regression and the Prophet forecasting model - both integral components of a data science workflow. The analysis and projections are grounded in empirical data from the Vietnamese education sector, sourced from the General Statistics Office of Vietnam. Data processing and forecasting were conducted using Jupyter notebooks within the Google Colab environment, incorporating various machine learning models. The study provides enrollment projections for provinces and cities across Vietnam up to the year 2030.