A machine learning approach to understand the Amazon buy box mechanism

Eryılmaz, Emre (2022) A machine learning approach to understand the Amazon buy box mechanism. [Thesis]

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Amazon marketplace is the leading e-commerce company globally. One of the most important features of the marketplace is a product can be offered to the customers by more than one seller. One of these sellers is selected by Amazon as the buy box winner on the product details page. Winning the buy box position is very important to a seller because more than 80% of the sales occur by buy box sellers. In this thesis, we developed a machine learning approach to understand the Amazon Buy Box mechanism. We have gathered the data set via Amazon AnyOfferChangedNotification API. The data set consists of the lowest twenty offers of a product and features of the sellers with the gathering time of the data set which is publicly available. We have developed supervised machine learning classification models which are Random Forest, XGBoost, and LightGBM to predict buy box winners. We have applied hyperparameter tuning and several subset selection techniques. These models reflected over 97% of accuracy for selected products. XGBoost model performed slightly higher than other models in terms of accuracy, precision, recall, and f1 score.
Item Type: Thesis
Uncontrolled Keywords: Amazon. -- buy box. -- machine learning.i -- classification. -- Random Forest. -- XGBoost. -- LightGBM. -- hyperparameter tuning. -- subset selection. -- Amazon, makine öğrenimi. -- sınıflandırma. -- Random Forest. -- hiperparametre ayarlama. -- alt küme seçimi.
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD0028 Management. Industrial Management
Divisions: Sabancı Business School
Depositing User: Dila Günay
Date Deposited: 13 Mar 2023 11:20
Last Modified: 01 Nov 2023 14:05
URI: https://research.sabanciuniv.edu/id/eprint/45502

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