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Predicting Ompact of Product and User Features on the Sales in an E-Commerce Site
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Boyacı, Samet; Yıldırım, İrem Zeynep
In recent years, the ratio of online shopping to total shopping has been increasing continuously. Many factors affect sales of e-commerce sites. Prior to purchasing, users are concerned whether the features of the products they are interested in match their own needs or not. In this study, the most important factors in the sales of the products which are the features of the products, attributes of the sellers and interactions with product investigated. A model was developed based on available fashion products in a market where individual users could sell second-hand textiles and accessories. Using this model, we tried to predict which products would be sold by examining at the features of the products, attributes of sellers and interactions with the product. Different algorithms were investigated for predicting sales and the results were reported. By comparing the outputs, the most successful algorithm and the most important features affecting sales were identified. As a result of this study, it was determined that the most efficient algorithm was the decision tree model. When the inputs of the model were examined, it was determined that the most important features affecting salability were the interactions with the products such as the number of likes and bids.

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