Cross-platform application of predictive computational model
DOI:
https://doi.org/10.33936/isrtic.v7i2.5832Keywords:
Computational model, flask, model as a service, API modelAbstract
The objective of this research was to carry out an application that allows the integration of a computational model to a multiplatform application, for which data was taken, the information from a supervised learning classification model regarding the collection of information on student dropout in the context of of the covid-19, this research was developed at the Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López. The methodology that was proposed for the respective development was Extreme programming (XP). Starting from a trained model, for which the python-based flask framework was used, then we proceeded to create a service to consume the model, later the respective tests were carried out using postman as a web client. The main result of the research was that the Flask Framework allowed the development and implementation of a RES API in a much more agile and easy way for the consumption of data from the predictive model.
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Aparicio, Q., Camilo, J., Chacón, C., & Yamile, L. (2022). Factores de deserción estudiantil School dropout factors. Revista Matices Tecnológicos Edición (Vol. 14).
Bonales-Daimel, G., & Mañas-Viniegra, L. (2021). The evolution of advertising investment during a decade of economic crisis (2007-2018). The case of the automotive sector and its adaptation to the Internet. Revista Mediterranea de Comunicacion (Vol. 12, Issue 2, pp. 227–243). Universidad de Alicante. https://doi.org/10.14198/MEDCOM.18155
Cedeno-Moreno, D., & Vargas, M. (2020). Aprendizaje automático aplicado al análisis de sentimientos. I+D Tecnológico, 16(2), 59-66. https://doi.org/10.33412/idt.v16.2.2833
Díaz, E., Romero, M., Faouzi, T., & Pardo, C. (2022). Modelos predictivos de la competencia pedagógica en docentes de EMTP mediante la minería de datos educacionales. Estudios pedagógicos (Valdivia), 48(2), 179-197. https://dx.doi.org/10.4067/S0718-07052022000200179
Gallegos, Juan J., Gonzalo Bach1 , Luza R. Luna, Bach1 , Sulla-Torres José, Gomez-Campos, Rossana and Marco Cossio-Bolaños (2020). Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy. LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering, Integration, and Alliances for a Sustainable Development”, 29-31 July 2020, Buenos Aires, Argentina.
García Ojalvo, I., Galarza López, J., & Sepúlveda, R. (2023). Modelo computacional de apoyo al proceso de ingreso a la educación superior cubana. Revista Cubana De Educación Superior, 41(1 Especial), 193–208. Recuperado a partir de https://revistas.uh.cu/rces/article/view/2696
Ghimire, D. (2020). Comparative study on Python web frameworks: Flask and Django. Metropolia University of Applied Sciences. Bachelor of Engineering.
González, Johans (2021) Algoritmos de Aprendizaje Supervisado en la Clasificación de Exoplanetas en Python. http://repositorio.uan.edu.co/handle/123456789/5839. Universidad Antonio Mariño. Facultad de Ingeniería Mecánica, Electrónica y Biomédica.
HashDork (2023) Las 8 mejores herramientas de análisis predictivo (código abierto). Consultado: 28/03/2023. https://hashdork.com/es/best-predictive-analysis-tools-open-source/
Jiménez Builes, J. A., Ramírez Bedoya, D. L., & Branch Bedoya, J. W. (2019). Metodología de desarrollo de software para plataformas educativas robóticas usando ROS-XP. Revista Politécnica, 15(30), 55–69. https://doi.org/10.33571/rpolitec.v15n30a6.
Relan, K. (2019) Creación de API REST con Flask. Apress. DOI https://doi.org/10.1007/978-1-4842-5022-8.
Romero , S., Hernández, I., Barrera, R. y Mendoza, A. (2022). Inteligencia emocional y desempeño académico en el área de las matemáticas durante la pandemia. Universidad del Zulia.
Spositto, O. M., Etcheverry, M. E., Ryckeboer, H. L., & Bossero, J. (2022). Aplicación de técnicas de minería de datos para la evaluación del rendimiento académico y la deserción estudiantil.
Tepepam, A., Pérez, H., Nakano, M. (2018) Algoritmos de aprendizaje supervisado para la clasificación de géneros musicales caracterizados mediante modelos estadísticos. Research in Computing Science 147(5).
Zavala Diaz, A. G. (2020). Sistema de respaldo de datos automatizado en la nube de amazon web services para evitar la posible pérdida de información en la empresa nessus Hoteles S.A. Universidad Cientifica, 1(1), 1–2.
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Copyright (c) 2023 Jéssica Johanna Morales Carrillo, Luis Cristóbal Cedeño Valarezo, Victor Joel Pinargote Bravo, Jesús Stefano Cajape Bravo , Jonathan Geovanny Ormaza Calderón
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