Comparative Analysis of Classification Techniques to Select Potential Female Applicants to Computer Related Careers in Northern Chile

Abstract

Computer-related careers have maintained the stigma of being mostly masculine. Currently, such careers are demanded in labour market, but there are not enough professionals to meet demand and less than 20% of the students enrolled in technology-related careers are women, according to the Chilean Higher Education Information Services. The absence of information that characterizes the women who enroll in the Computer related area in Chile is the main motivation for this work.This study presents a comparison of the results of classification techniques for the data set of female students who choose Computer Science in universities belonging to the Council of Rectors of Chilean Universities (CRUCH), in order to identify relevant variables to choose this carrers. School location, academic performance, and mother's education were relevant. The results of two resampling schemes for imbalanced classes are similar, however Naiive Bayes with undersampling obtained slightly more balanced results with Prediction of 61%.