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People Thoughts Prediction Using Machine Learning on Online Shopping in Bangladesh During COVID-19 Pandemic

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dc.contributor.author Akter, Sharmin
dc.contributor.author Islam, MD. Khairul
dc.contributor.author Hossain, MD. Nabir
dc.contributor.author Rahman, Mahfuzur
dc.contributor.author Boshra, Syeda Jannatul
dc.date.accessioned 2024-03-04T09:46:13Z
dc.date.available 2024-03-04T09:46:13Z
dc.date.issued 2023-04-28
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11636
dc.description.abstract E-commerce business has become a prominent entity of global retail as online transaction saves time and cost at the same time. COVID-19 pandemic and lockdown accelerated the growth of e-commerce. The new e-business companies are booming rapidly whereas insincerity in customer concern is noticeable. As a result, the purchasers are facing numerous problems while buying online. The main objective of this study is to predict preferences on online shopping of buyers and based on that analysis, the pattern can be observed. While doing the study, we used some popular Supervised machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naıve Bayes (NB) algorithm. Amongst those, best accuracy was delivered by the Decision Tree algorithm. The output clearly demonstrates that, people are more likely to participate in online shopping if the obstacles could be alleviate which means, buyers are still not satisfied and confident about the online platform. Hopefully, the result of this study can be a great asset for improving the E-commerce sector of Bangladesh if it is optimized wisely. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Online shopping en_US
dc.subject COVID-19 en_US
dc.subject Pandemic situation en_US
dc.subject E-commerce en_US
dc.title People Thoughts Prediction Using Machine Learning on Online Shopping in Bangladesh During COVID-19 Pandemic en_US
dc.type Article en_US


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