Predicting future admission figure of an university Master’s programme

To predict the future admission number of an University's Master's programme

Tools: Python (pandas, machine learning libraries), Google Looker Studio, Microsoft Excel

Keywords: EDA, Python, machine learning, data visualization, tertiary education, admission

With the emergence of artificial intelligence (AI) and the decline of birthrate in developed countries, the field of education is currently facing tremendous uncertainty as to how this field should evolve in the future. But no changes could be made without funding, and students' tuition fee are one big source of income for universities' programme. Will students' admission number be influenced by external factor outside of the university campus, such as employment markets and unemployment rate?

I have created a project using a dataset of student admission figures of an university's Master's programme that recorded student admission number of each year in a 14-year span. The project uses Python for data cleaning and determining what machine learning models to be used, and Google Looker Studio to design a dashboard for showing the current and future predicted number of students to be admitted in this programme.

For the detailed story and how I approach this project, please read the story here.

For accessing the files used for this project, please visit this GitHub repository.