Classification of gender equalizing factors by C5.0, CART, CHAID analysis methods

In this study data obtained from Global Gender Inequality Report, it is aimed to examine magnitude of gender based inequalities with the numbers and depending on the change of time. For this purpose, the four main functions (health, education, economy and politics) that make up the profile of the country are used. Data mining classification methods were used in the study. A panel data set was created for 144 countries covering the period 2006-2016 and these data are obtained from reports in the World Economic Form. Decision tree made by analysis of CART, C5.0 and CHAID algorithms and that the ratio that would reveal the countries' gender inequality ratio was developed or not developed by the effect of the variable. According to the results of analysis using C5.0, CART, CHAID algorithms the most important variables are X5 (Professional and technical workers) variable and X4 (lawmakers, authorities and executives) variable and these two variables form the first division point of the root node and X13 (women in administrative position) variable was determined to be the second division of the decision tree to form the other division. Keywords: Gender inequality, data mining, classification.