Support Vector Machines with Hyperparameter Optimization Frameworks for Classifying Mobile Phone Prices in Multi-Class

Accurately predicting mobile phone prices is essential for improving consumer decision-making, supporting business strategies, and enhancing market transparency. However, studies on improving the performance of multi-class classification models by using hyperparameter selection frameworks are limited. Thus, this study aims to develop a mobile phone price classification model by integrating support vector machines (SVM) with two advanced hyperparameter optimization (HPO) frameworks, namely Hyperopt (HYP) and Optuna (OPT), for hyperparameter determination to increase classification accuracy. A public dataset with various training and testing conditions is used by presented models, namely SVMHYP and SVMOPT models. Numerical results indicate that the developed models outperform results from the previous literature in terms of classification accuracy. Furthermore, a 5-fold cross-validation strategy is performed to examine generalizability and robustness of the presented multi-classification models. These findings highlight the effectiveness of combining SVM with HPO as a robust solution for mobile phone price prediction.