Exploring the gender inequality gap: Pipelines for open source data management, a path to AI practice

Ana Luna, Pilar Hidalgo-Leon, Rafael Ricardo Rentería, Andrea Montaño Ramírez

Research output: Contribution to journalArticle in a journalpeer-review

Abstract

This research article explains in detail the pre-processing stage unifying various techniques, using real and open public data from Peru, between the years 2016-2019. The main objective is to address the study of gender inequality through clean and reliable data. This article shows how to group and clean 6 data sets by category to identify and interpret inequality factors, extract valuable information that can be used in data mining models, and contribute to future decision making. The pre-processing techniques were validated using various prediction algorithms and their performances were compared using ranking metrics.
Original languageEnglish
Pages (from-to)4251-4276
JournalJournal of Positive School Psychology
Volume6
Issue number10
StatePublished - 2022

Keywords

  • Pre-processing
  • Gender inequality
  • Data mining
  • Classifiers

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