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Study on the Performance of Supervised Machine Learning Algorithms in Mobile Price Range Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 1 |
| Year of Publication: 2025 |
| Authors: A.S.M. Sabiqul Hassan, Syed Maruful Huq, Mohammad Kamal Hossain Foraji, Md. Humayun Kabir |
10.5120/ijca2025924768
|
A.S.M. Sabiqul Hassan, Syed Maruful Huq, Mohammad Kamal Hossain Foraji, Md. Humayun Kabir . Study on the Performance of Supervised Machine Learning Algorithms in Mobile Price Range Classification. International Journal of Computer Applications. 187, 1 ( May 2025), 39-45. DOI=10.5120/ijca2025924768
Abstract
The prior prediction of the mobile price range based on different features can help potential customers to purchase their target mobile phones. It also helps manufacturers to develop a decision-making model in setting up the price range, e.g., very economical, economical, expensive and very expensive of upcoming mobile phones with different features. This paper explores some machine learning algorithms and their application in classifying of mobile phone price ranges by analyzing a dataset collected from the Kaggle online dataset repository. The dataset was divided into three partitions where a train set consisting of 70% data, validation set and test set each sharing the remaining 30% data equally. Then, different classification algorithms: K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Logistic Regression (LR) were applied to the dataset to develop machine learning models. Finally, SVM model achieved the highest F1 score of 97% among the developed machine learning models. The knowledge extracted from that model can be used as a decision-making tool for predicting the prices of mobile phones and classifying their range in the future.
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