Year: 2024
Type: article
Abstract: This paper explores the use of two machine learning models, Decision Tree and Support Vector Regression (SVR), for smartphone price prediction. Decision Trees provide a straightforward and efficient classification method, while SVR specializes in managing complex relationships for regression tasks. The study compares the performance of these models in predicting smartphone prices, analyzing key factors such as processor speed, memory, and battery capacity. Additionally, a combined model approach that combines Decision Tree for classification and SVR for regression is proposed to improve prediction accuracy. The results suggest that while Decision Tree performs better in classification tasks, the combination of both models demonstrates potential for more precise price predictions, particularly for low-priced smartphones.
Author: Xianzong Li
Institution: University of California, Riverside
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405592696