Application of the performance of machine learning techniques as support in the prediction of school dropout
Abstract This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53576-1 |
| _version_ | 1797274504805744640 |
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| author | Auria Lucia Jiménez-Gutiérrez Cinthya Ivonne Mota-Hernández Efrén Mezura-Montes Rafael Alvarado-Corona |
| author_facet | Auria Lucia Jiménez-Gutiérrez Cinthya Ivonne Mota-Hernández Efrén Mezura-Montes Rafael Alvarado-Corona |
| author_sort | Auria Lucia Jiménez-Gutiérrez |
| collection | DOAJ |
| description | Abstract This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%. |
| first_indexed | 2024-03-07T14:59:21Z |
| format | Article |
| id | doaj.art-55b33bdf9de846c19661faa0703f1fc9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T14:59:21Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-55b33bdf9de846c19661faa0703f1fc92024-03-05T19:12:36ZengNature PortfolioScientific Reports2045-23222024-02-011411810.1038/s41598-024-53576-1Application of the performance of machine learning techniques as support in the prediction of school dropoutAuria Lucia Jiménez-Gutiérrez0Cinthya Ivonne Mota-Hernández1Efrén Mezura-Montes2Rafael Alvarado-Corona3Centro Universitario de los Lagos, Universidad de GuadalajaraUniversidad del Valle de MéxicoInstituto de Investigaciones en Inteligencia Artificial, Universidad VeracruzanaCentro de Estudios Tecnológicos Industrial y de Servicios N°06 “Ignacio Manuel Altamirano” Abstract This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.https://doi.org/10.1038/s41598-024-53576-1 |
| spellingShingle | Auria Lucia Jiménez-Gutiérrez Cinthya Ivonne Mota-Hernández Efrén Mezura-Montes Rafael Alvarado-Corona Application of the performance of machine learning techniques as support in the prediction of school dropout Scientific Reports |
| title | Application of the performance of machine learning techniques as support in the prediction of school dropout |
| title_full | Application of the performance of machine learning techniques as support in the prediction of school dropout |
| title_fullStr | Application of the performance of machine learning techniques as support in the prediction of school dropout |
| title_full_unstemmed | Application of the performance of machine learning techniques as support in the prediction of school dropout |
| title_short | Application of the performance of machine learning techniques as support in the prediction of school dropout |
| title_sort | application of the performance of machine learning techniques as support in the prediction of school dropout |
| url | https://doi.org/10.1038/s41598-024-53576-1 |
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