Mobile Phone Price Classification Using Machine Learning


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Authors

  • Seda İpek AKSOY ERCAN Ostim Technical University
  • Murat ŞİMŞEK Ostim Technical University

DOI:

https://doi.org/10.59287/ijanser.791

Keywords:

Phone Price Prediction, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree

Abstract

Smartphones are becoming more and more important for people day by day. With the development of technology, telephones are used in many areas from daily life to business life. It does not only fulfil the function of calling someone. It also makes it possible to connect to the internet and read mail while away from the computer. For this reason, the features of the mobile phone are an important factor when purchasing a mobile phone. People who actively use the phone pay more attention to feature selection. When buying a cell phone, price-performance comparison is made. Phone features are considered as performance. There is no exact method for determining the price. Price estimation can be done with the use of machine learning algorithms. Many studies have been conducted on this subject in the literature. There are still ongoing studies on which machine learning algorithm is the most appropriate. In order to contribute to this issue, a price prediction study is conducted on different machine learning algorithms. Using a dataset of phone prices and features from Kaggle, 4 different models with 20 features are tackled. As a result, the highest value is obtained from Support Vector Machine with an accuracy value of 0.9616.

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Author Biographies

Seda İpek AKSOY ERCAN, Ostim Technical University

Computer Engineering Department, Türkiye

Murat ŞİMŞEK, Ostim Technical University

Artificial Intelligence Engineering Department, Türkiye

References

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Published

2023-05-30

How to Cite

AKSOY ERCAN, S. İpek, & ŞİMŞEK, M. (2023). Mobile Phone Price Classification Using Machine Learning. International Journal of Advanced Natural Sciences and Engineering Researches, 7(4), 458–462. https://doi.org/10.59287/ijanser.791

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