Predicting IT employability using data mining techniques

Abstract

Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables. First, different classification algorithms in data mining were tested making logistic regression with accuracy of 78.4 is implemented. Based on logistic regression analysis, three academic variables directly affect; IT_Core, IT_Professional and Gender identified as significant predictors for employability. The data were collected based on the five year profiles of 515 students randomly selected at the placement office tracer study.