Predicción para el negocio de alquiler de automóviles con técnicas supervisadas

Sandra Zapata-Quentasi; Alba Yauri-Ituccayasi; Rodrigo Huamani-Avendaño; Jose Alfredo Sulla Torres

doi:10.18687/LACCEI2019.1.1.371

Predicción para el negocio de alquiler de automóviles con técnicas supervisadas

Sandra Zapata-Quentasi, Alba Yauri-Ituccayasi, Rodrigo Huamani-Avendaño, Jose Alfredo Sulla Torres

Departamento Académico de Ingeniería de Sistemas e Informática

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Car rental is a new trend and is already a reality in many countries, as it is a cheaper option than maintaining your own. The objective of this article is to identify the ideal car for a person, according to the characteristics that you want. In the present work, a study was made of the previous steps involved in the prediction of a car according to the desired characteristics and a comparison of the classification algorithms was carried out to determine which classification is appropriate in terms of the accuracy of the prediction. The steps followed were: Data collection, preprocessing, data preparation and comparison of classification algorithms. The results obtained show that the Random Forest algorithm presents a 95.12% correct classification of the instances and a mean square error of 0.12, which are acceptable results for the tests performed.

Título traducido de la contribución	Prediction for the car rental business with supervised techniques
Idioma original	Español
Título de la publicación alojada	17th LACCEI International Multi-Conference for Engineering, Education, and Technology
Subtítulo de la publicación alojada	"Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019
Editorial	Latin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)	9780999344361
DOI	https://doi.org/10.18687/LACCEI2019.1.1.371
Estado	Publicada - 2019
Evento	17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019 - Montego Bay, Jamaica Duración: 24 jul. 2019 → 26 jul. 2019

Serie de la publicación

Nombre	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen	2019-July
ISSN (versión digital)	2414-6390

Conferencia

Conferencia	17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019
País/Territorio	Jamaica
Ciudad	Montego Bay
Período	24/07/19 → 26/07/19

Nota bibliográfica

Publisher Copyright:
© 2019 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.

Palabras clave

Car rental
KDD
Prediction
Supervised techniques

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10.18687/LACCEI2019.1.1.371

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Zapata-Quentasi, S., Yauri-Ituccayasi, A., Huamani-Avendaño, R., & Sulla Torres, J. A. (2019). Predicción para el negocio de alquiler de automóviles con técnicas supervisadas. En 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019 (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2019-July). Latin American and Caribbean Consortium of Engineering Institutions. https://doi.org/10.18687/LACCEI2019.1.1.371

Zapata-Quentasi, Sandra ; Yauri-Ituccayasi, Alba ; Huamani-Avendaño, Rodrigo et al. / Predicción para el negocio de alquiler de automóviles con técnicas supervisadas. 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019. Latin American and Caribbean Consortium of Engineering Institutions, 2019. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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title = "Predicci{\'o}n para el negocio de alquiler de autom{\'o}viles con t{\'e}cnicas supervisadas",

abstract = "Car rental is a new trend and is already a reality in many countries, as it is a cheaper option than maintaining your own. The objective of this article is to identify the ideal car for a person, according to the characteristics that you want. In the present work, a study was made of the previous steps involved in the prediction of a car according to the desired characteristics and a comparison of the classification algorithms was carried out to determine which classification is appropriate in terms of the accuracy of the prediction. The steps followed were: Data collection, preprocessing, data preparation and comparison of classification algorithms. The results obtained show that the Random Forest algorithm presents a 95.12% correct classification of the instances and a mean square error of 0.12, which are acceptable results for the tests performed.",

keywords = "Car rental, KDD, Prediction, Supervised techniques",

author = "Sandra Zapata-Quentasi and Alba Yauri-Ituccayasi and Rodrigo Huamani-Avenda{\~n}o and {Sulla Torres}, {Jose Alfredo}",

year = "2019",

doi = "10.18687/LACCEI2019.1.1.371",

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Zapata-Quentasi, S, Yauri-Ituccayasi, A, Huamani-Avendaño, R & Sulla Torres, JA 2019, Predicción para el negocio de alquiler de automóviles con técnicas supervisadas. En 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, vol. 2019-July, Latin American and Caribbean Consortium of Engineering Institutions, 17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019, Montego Bay, Jamaica, 24/07/19. https://doi.org/10.18687/LACCEI2019.1.1.371

Predicción para el negocio de alquiler de automóviles con técnicas supervisadas. / Zapata-Quentasi, Sandra; Yauri-Ituccayasi, Alba; Huamani-Avendaño, Rodrigo et al.

17th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019. Latin American and Caribbean Consortium of Engineering Institutions, 2019. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2019-July).

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Predicción para el negocio de alquiler de automóviles con técnicas supervisadas

AU - Zapata-Quentasi, Sandra

AU - Yauri-Ituccayasi, Alba

AU - Huamani-Avendaño, Rodrigo

AU - Sulla Torres, Jose Alfredo

PY - 2019

Y1 - 2019

N2 - Car rental is a new trend and is already a reality in many countries, as it is a cheaper option than maintaining your own. The objective of this article is to identify the ideal car for a person, according to the characteristics that you want. In the present work, a study was made of the previous steps involved in the prediction of a car according to the desired characteristics and a comparison of the classification algorithms was carried out to determine which classification is appropriate in terms of the accuracy of the prediction. The steps followed were: Data collection, preprocessing, data preparation and comparison of classification algorithms. The results obtained show that the Random Forest algorithm presents a 95.12% correct classification of the instances and a mean square error of 0.12, which are acceptable results for the tests performed.

AB - Car rental is a new trend and is already a reality in many countries, as it is a cheaper option than maintaining your own. The objective of this article is to identify the ideal car for a person, according to the characteristics that you want. In the present work, a study was made of the previous steps involved in the prediction of a car according to the desired characteristics and a comparison of the classification algorithms was carried out to determine which classification is appropriate in terms of the accuracy of the prediction. The steps followed were: Data collection, preprocessing, data preparation and comparison of classification algorithms. The results obtained show that the Random Forest algorithm presents a 95.12% correct classification of the instances and a mean square error of 0.12, which are acceptable results for the tests performed.

KW - Car rental

KW - KDD

KW - Prediction

KW - Supervised techniques

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DO - 10.18687/LACCEI2019.1.1.371

M3 - Contribución a la conferencia

AN - SCOPUS:85073617254

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology

PB - Latin American and Caribbean Consortium of Engineering Institutions

Y2 - 24 July 2019 through 26 July 2019

ER -

Zapata-Quentasi S, Yauri-Ituccayasi A, Huamani-Avendaño R, Sulla Torres JA. Predicción para el negocio de alquiler de automóviles con técnicas supervisadas. En 17th LACCEI International Multi-Conference for Engineering, Education, and Technology: "Industry, Innovation, and Infrastructure for Sustainable Cities and Communities", LACCEI 2019. Latin American and Caribbean Consortium of Engineering Institutions. 2019. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/LACCEI2019.1.1.371

Predicción para el negocio de alquiler de automóviles con técnicas supervisadas

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Palabras clave

Acceder al documento

Otros archivos y enlaces

Huella

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