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

doi:10.5083/ejcm

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Research Article | Volume 9 Issue :2 (, 2019) | Pages 1 - 6

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

Under a Creative Commons license

Open Access

DOI : 10.5083/ejcm

Published

June 25, 2019

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.

Keywords

Pre-processing

Gender inequality

Data mining

Classifiers

Predictive algorithms.

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