research-article

Prediction Demand for Classified Ads Using Machine Learning: an Experiment Study

Authors:

Marouane Adnane,

Sanaa El Fkihi,

Rachid Oulad Haj ThamiAuthors Info & Claims

NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

Article No.: 39, Pages 1 - 6

https://doi.org/10.1145/3320326.3320371

Published: 27 March 2019 Publication History

Abstract

Classified ads prediction is a very interesting activity for organizations in order to increase the purchase quantity of a product and thereafter the possibility of sale. Used goods predicting can be done by calculating the probability of sale for each selected product. In this paper, we conduct an empirical analysis on classified ads prediction of Avito dataset in order to develop prediction models using three individual machine-learning techniques and five ensemble learners. We compare and evaluate the performance of the proposed models using Root Mean Square Error (RMSE) measure. The stacked generalization method was also used to combine the best-performed models to select the best one. The results show that the Extreme Gradient Boosting Machine algorithm (XGBoost) is the most accurate model with an RMSE value of 0.2253.

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Cited By

Adnane MNguyen CKhoumsi ATrovão J(2024)Real-Time Torque-Distribution for Dual-Motor Off-Road Vehicle Using Machine Learning ApproachIEEE Transactions on Vehicular Technology10.1109/TVT.2024.335518673:4(4567-4577)Online publication date: Apr-2024
https://doi.org/10.1109/TVT.2024.3355186
Adnane MKhoumsi ATrovão J(2023)Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive SurveyEnergies10.3390/en1613489716:13(4897)Online publication date: 23-Jun-2023
https://doi.org/10.3390/en16134897
Adnane MNguyen CKhoumsi ATrovão J(2023)Torque Distribution Prediction for Dual-Motor Electric Vehicle Using Ensemble Learning Algorithms2023 IEEE Vehicle Power and Propulsion Conference (VPPC)10.1109/VPPC60535.2023.10403179(1-6)Online publication date: 24-Oct-2023
https://doi.org/10.1109/VPPC60535.2023.10403179
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cover image ACM Other conferences

NISS '19: Proceedings of the 2nd International Conference on Networking, Information Systems & Security

March 2019

512 pages

ISBN:9781450366458

DOI:10.1145/3320326

Editors:
Badr Abouelmajd
FSR, Mohammed V University, Morocco
,
Mohamed Ben Ahmed
FSTT, Abdelmalek Essaâdi University, Morocco
,
Boudhir Anouar Abdelhakim
FSTT, Abdelmalek Essaâdi University, Morocco
,
Hassan El Ghazi
INPT, Morocco

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 March 2019

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NISS19: Networking, Information Systems & Security

March 27 - 29, 2019

Rabat, Morocco

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Cited By

Adnane MNguyen CKhoumsi ATrovão J(2024)Real-Time Torque-Distribution for Dual-Motor Off-Road Vehicle Using Machine Learning ApproachIEEE Transactions on Vehicular Technology10.1109/TVT.2024.335518673:4(4567-4577)Online publication date: Apr-2024
https://doi.org/10.1109/TVT.2024.3355186
Adnane MKhoumsi ATrovão J(2023)Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive SurveyEnergies10.3390/en1613489716:13(4897)Online publication date: 23-Jun-2023
https://doi.org/10.3390/en16134897
Adnane MNguyen CKhoumsi ATrovão J(2023)Torque Distribution Prediction for Dual-Motor Electric Vehicle Using Ensemble Learning Algorithms2023 IEEE Vehicle Power and Propulsion Conference (VPPC)10.1109/VPPC60535.2023.10403179(1-6)Online publication date: 24-Oct-2023
https://doi.org/10.1109/VPPC60535.2023.10403179
Birim ŞKazancoglu IKumar Mangla SKahraman AKumar SKazancoglu Y(2022)Detecting fake reviews through topic modellingJournal of Business Research10.1016/j.jbusres.2022.05.081149(884-900)Online publication date: Oct-2022
https://doi.org/10.1016/j.jbusres.2022.05.081
Birim SKazancoglu IMangla SKahraman AKazancoglu Y(2022)The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methodsAnnals of Operations Research10.1007/s10479-021-04429-x339:1-2(131-161)Online publication date: 7-Jan-2022
https://doi.org/10.1007/s10479-021-04429-x
Sheikhi S(2021)An effective fake news detection method using WOA-xgbTree algorithm and content-based featuresApplied Soft Computing10.1016/j.asoc.2021.107559109:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107559

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