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 language | English |
|---|---|
| Pages (from-to) | 4251-4276 |
| Journal | Journal of Positive School Psychology |
| Volume | 6 |
| Issue number | 10 |
| State | Published - 2022 |
Keywords
- Pre-processing
- Gender inequality
- Data mining
- Classifiers
