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
N1 - Publisher Copyright:
© 2019 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
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
UR - http://www.scopus.com/inward/record.url?scp=85073617254&partnerID=8YFLogxK
U2 - 10.18687/LACCEI2019.1.1.371
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
T2 - 17th LACCEI International Multi-Conference for Engineering, Education, and Technology, LACCEI 2019
Y2 - 24 July 2019 through 26 July 2019
ER -