Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest

In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furt...

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Detalles Bibliográficos
Autores principales: Baati, Karim, Mohsil, Mouad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256375/
http://dx.doi.org/10.1007/978-3-030-49161-1_4
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author Baati, Karim
Mohsil, Mouad
author_facet Baati, Karim
Mohsil, Mouad
author_sort Baati, Karim
collection PubMed
description In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques.
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spelling pubmed-72563752020-05-29 Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest Baati, Karim Mohsil, Mouad Artificial Intelligence Applications and Innovations Article In this paper, we suggest a real-time online shopper behavior prediction system which predicts the visitor’s shopping intent as soon as the website is visited. To do that, we rely on session and visitor information and we investigate naïve Bayes classifier, C4.5 decision tree and random forest. Furthermore, we use oversampling to improve the performance and the scalability of each classifier. The results show that random forest produces significantly higher accuracy and F1 Score than the compared techniques. 2020-05-06 /pmc/articles/PMC7256375/ http://dx.doi.org/10.1007/978-3-030-49161-1_4 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Baati, Karim
Mohsil, Mouad
Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title_full Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title_fullStr Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title_full_unstemmed Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title_short Real-Time Prediction of Online Shoppers’ Purchasing Intention Using Random Forest
title_sort real-time prediction of online shoppers’ purchasing intention using random forest
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256375/
http://dx.doi.org/10.1007/978-3-030-49161-1_4
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AT mohsilmouad realtimepredictionofonlineshopperspurchasingintentionusingrandomforest

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