Predicting Smart Tablet Preferences in Turkish E-Commerce Platforms Using Artificial Neural Networks and Machine Learning Techniques
Source abstract
Abstract
This study aims to predict Turkish consumer preferences for smart tablets on e-commerce platforms, focusing on consumer behavior in a developing country context. Key product attributes—such as processor speed, screen size, internal storage capacity, display resolution, RAM, processor core count, and battery capacity—were collected from major e-commerce websites in Turkey. Data analysis indicated that consumers predominantly prefer tablets with processor speeds between 1–3 GHz, internal storage capacities of 32–64 GB, 2–3 GB of RAM, screen sizes of 7–11 inches, and battery capacities between 5001–8000 mAh. To predict the most preferred tablet configurations, Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), and Random Forest (RF) models were developed and evaluated. Among these, the ANN model achieved the highest prediction accuracy, particularly regarding RAM preferences. The findings contribute to the growing body of research on consumer behavior modeling in emerging markets and may assist manufacturers and marketers in shaping strategic decisions related to product development and online retail strategies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Consumer Attitudes and Food Labeling · Consumer Packaging Perceptions and Trends · Technology Adoption and User Behaviour
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Engineering, Faculty of Engineering and Architecture, Sinop University, 57000 Sinop, Turkey
References (100+)
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