Application of Random Forest Algorithm for the Prediction of Online Food Delivery Service Delay

Abstract

Online shopping industry nowadays has been growing rapidly with the evolution of technology. Consumers have started to shop according to certain criteria with the spread of the online shopping sector. One of the sectors that enlightens the future customer in terms of service quality by getting feedback from purchases (comments or ratings) is the online food sector. In this study, a classification study is conducted to investigate the observance with the fast delivery criteria, which is one of the cornerstone criteria in the online food industry. Random Forest (RF) algorithm is applied for the classification. The most important advantage of the RF is it handles a large number of input variables also it is speediness. More than that RF algorithm reduces the overfitting problem and as a result variance is small and therefore it improves the accuracy. The application is implemented through R programming language. In this study, online food delivery variable is created as two categories (On time or Early and Late) and is estimated by RF algorithm that is applied this data for the first time. According to the results, the correct classification rate of the testing data for the estimation of the online food delivery status variable is found as 95.85%. In addition, the performances of the restaurants are compared for the customers. It turns out that the traffic situation does not greatly affect the result of the delivery status. As a result, RF algorithm is applied to the data obtained by web scraping techniques and the delivery status performance of restaurants is revealed with this study.

Keywords

• ensemble learning
• random forest
• online food ordering

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