This research aims at using predictive models that enable us to predict students who are at risk of dropping out and identify the factors that possibly lead to this dropout. Through the results obtained, concerned stakeholders will be able to effectively develop strategies and initiatives to help decrease the percentage of students’ attrition. There are different reasons why students drop from their courses which could be related to academic issues or personal issues that stop them from being active students. Due to these many reasons of students dropping out, universities are impacted negatively in terms of the financial costs as they lose an amount of money from those students, and sometimes they lose the funds from public sponsors to major activities in universities. The proposal aims at exploring the various reasons that influence students’ decision to withdraw and what will be the best model for the prediction. I will use data from the open-source Kaggle and use Python to explore and preprocess the data. I will also use Tableau for getting visual insights from the available dataset.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
AlHashemi, Zainab, "Using Prediction ML algorithm for predicting early Student Attrition in Higher Education" (2021). Thesis. Rochester Institute of Technology. Accessed from