Comparison of KNN and DNN Classifiers Performance in Predicting Mobile Phone Price Ranges

Ercüment GÜVENÇ; Gürcan ÇETİN; Hilal KOÇAK

Makale detayları

Comparison of KNN and DNN Classifiers Performance in Predicting Mobile Phone Price Ranges

Yazarlar:

Ercüment GÜVENÇ | Gürcan ÇETİN | Hilal KOÇAK

Yayıncı:

Osman ÖZKARACA

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Advances in Artificial Intelligence Research 2021 Vol.1 Issue 1, pp.19-28

Belge Tipi:

Makale

Yayın Tarihi:

1/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000447268

Sıklık:

İki Yıllık

pISSN:

2757-7422

Özet:

 

The aim of this study was to compare the classification performances of K-nearest neighbors (KNN) and Deep Neural Networks (DNN) models in a dataset. For this purpose, the “mobile price forecast" dataset has been selected. It includes 20 different features of mobile phones such as battery power, Bluetooth function, memory capacity, screen size. The problem presented in the dataset is to determine the price range class before the mobile phones are released. Such a forecast will help companies that manufacture mobile devices to estimate the price of their mobile phones in a more convenient method to compete against their competitors in the market. In the implementation phase of the study, the KNN and DNN models were created in Python 3.6 with the Sklearn and Keras libraries. The classification performances of the models were determined using F-1 score, accuracy, recall and accuracy measurements. As a result of the study, validation data was classified with 92% accuracy using the trained DNN model. In addition, at another stage of the study, the price range for 1000 devices with different features was determined by using a test dataset that was not labeled, and the results produced by both models were compared.

Makale detayları

Comparison of KNN and DNN Classifiers Performance in Predicting Mobile Phone Price Ranges

Yazarlar: Ercüment GÜVENÇ | Gürcan ÇETİN | Hilal KOÇAK

Yayıncı: Osman ÖZKARACA

Ana Başlıklar: Özel bilgisayar yöntemleri

Dergi: Advances in Artificial Intelligence Research 2021 Vol.1 Issue 1, pp.19-28

Belge Tipi: Makale

Yayın Tarihi: 1/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000447268

Sıklık: İki Yıllık

pISSN: 2757-7422

Sayı

Yazarlar:

Ercüment GÜVENÇ | Gürcan ÇETİN | Hilal KOÇAK

Yayıncı:

Osman ÖZKARACA

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Advances in Artificial Intelligence Research 2021 Vol.1 Issue 1, pp.19-28

Belge Tipi:

Makale

Yayın Tarihi:

1/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000447268

Sıklık:

İki Yıllık

pISSN:

2757-7422