Mining of Completion Rate of Higher Education Based on Fuzzy Feature Selection Model and Machine Learning Techniques

In the context of the great change in the labor market and the higher education sector, great attention is given to individuals with an academic degree or the so-called graduates class. However, each educational institution has a different approach towards students who wish to complete their university degree. This study aims at (1) identifying the most important factors that directly affect the completion, and (2) predicting the completion rates of students for university degrees according to the system of higher education in the United States. Unlike previous studies, this project contributes to the use of the fuzzy logic technique on three methods for feature selection, namely the Correlation Attribute Evaluation, Relief Attribute Evaluation, and Gain Ratio Method. Since these three methods give different weight to the same attribute, the fuzzy logic technique has been used to get one weight for the attribute. A great challenge faced throughout this study is the curse of dimensionality, because the college scorecard dataset launched by the US Department of Education contains approximately (8000) educational institutions and (1825) features. Applying the method used in this study to identify important features lead to their reduction to only (79). Accordingly, two models have been used to predict the completion rates of students for their university studies which are the Random Forest and the Support Vector Regression with a Mean Absolute Error (MAE) value of (0.068) and (0.097) respectively.