EGENDERIN-EQUALITY-GAP

This research article explains in detail the pre-processing stage unifying various techniques, usingreal and open public data from Peru, between the years 2016-2019. The main objective is to addressthe study of gender inequality through clean and reliable data. This article shows how to group andclean 6 data sets by category to identify and interpret inequality factors, extract valuable informationthat can be used in data mining models, and contribute to future decision making. The pre-processingtechniques were validated using various prediction algorithms and their performances were comparedusing ranking metrics