| Autor: |
Hamami, Faqih, Muzakki, Ahmad, Alfiniyah, Cicik, Fatmawati, Windarto |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2020, Vol. 2329 Issue 1, p1-7, 7p |
| Abstrakt: |
Nowadays people prefer to buy online rather than buy on the spot. Online transactions make people's lives easier. This condition requires the seller to understand the characteristics of the intention of the prospective buyer. This research proposes a machine-learning pipeline to predict the customer behavior for e-commerce products. We compared several machine-learning algorithms to find the best algorithm to solve the problem then we deployed the model on a web application. Based on the experiment, the Random Forest algorithm can predict online shopper intention with 90% of accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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