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10.1007/s10614-020-10069-3 http://scihub22266oqcxt.onion/10.1007/s10614-020-10069-3 33169049!7643087!33169049     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Econ 2022 ; 59 (4): 1525-1538 Nephropedia Template TP {{tp|p=33169049|t=2022. A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic |pdf=|usr=}}{{33169049}} gab.com Text A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic JO: Comput Econ http://www.kidney.de/pget.php?&tw=1&pmid=33169049 #FOAvip The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic. Twit Text FOAVip A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic ????Comput Econ http://www.kidney.de/pget.php?&tw=1&pmid=33169049 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33169049 #FOAvip English Wikipedia <ref>{{Cite pmid|33169049}}</ref> A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic #MMPMID33169049 Safara F Comput Econ 2022[]; 59 (4): 1525-1538 PMID33169049show ga The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic. äA Computational Model to Predict Consumer Behaviour During COVID-19 Pa(epmc).pdf DeepDyve Pubget Overpricing