Türkiye'de E-Ticaretin Kullanılma Durumunun Makine Öğrenmesi İle Sınıflandırılması ve Çeşitli Değişkenlerle İlişkilerinin Analizi

Yıl 2024, Cilt: 16 Sayı: 31, 582 - 610, 31.12.2024

Yunus Emre Gür , Kamil Abdullah Eşidir , Cem Ayden

https://doi.org/10.38155/ksbd.1477120

Öz

Bu çalışmada, Türkiye İstatistik Kurumu’nun (TÜİK) 2023 yılında gerçekleştirdiği Hanehalkı Bilişim Teknolojileri Kullanımı Araştırması (HBTKA) verileri kullanılarak, e-ticaret kullanım durumunun makine öğrenmesi yöntemleri ile sınıflandırılma işlemi gerçekleştirilmiştir. Bununla birlikte, cinsiyet, yaş, eğitim durumu gibi demografik faktörler ile teknoloji kullanım durumu ve sosyal medya kullanımı gibi faktörlerin e-ticaret kullanımı ile ilişkileri analiz edilmiştir. Bu veri seti üzerinde, veri madenciliği sınıflandırma tekniklerinden karar ağaçları kullanılarak analiz yapılmıştır. Çalışmada, sınıflandırma işlemi için Rastgele Orman, En Yakın Komşular, Destek Vektör Makinesi, Lojistik Regresyon, Naive Bayes ve Gradient Boosting gibi çeşitli makine öğrenmesi modelleri kullanılmıştır. Analiz sonuçları, özellikle Gradient Boosting modelinin yüksek doğruluk oranıyla dikkat çekerek, e-ticaret kullanımının sınıflandırılmasında güçlü bir araç olduğunu göstermiştir. Çalışmada ayrıca, e-ticaret kullanımının iyileştirilmesine yönelik stratejiler önerilmektedir.

Anahtar Kelimeler

E-ticaret, Hanehalkı Bilişim Teknolojileri, Makine Öğrenmesi, Sınıflandırma Teknikleri, Veri Madenciliği.

CLASSIFICATION THE USE OF E-COMMERCE IN TURKEY WITH MACHINE LEARNING AND ANALYZING ITS RELATIONSHIP WITH VARIOUS VARIABLES

Öz

In this study, the classification of e-commerce usage status with machine learning methods was carried out using the Household Information Technology Usage Survey data conducted by the Turkish Statistical Institute in 2023. In addition, demographic factors such as gender, age, education level, technology usage status and social media usage were analyzed in relation to e-commerce usage. This data set was analyzed using decision trees, one of the data mining classification techniques. In the study, various machine learning models such as Random Forest, Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes and Gradient Boosting were used for classification. The results of the analysis showed that the Gradient Boosting model is a powerful tool for classifying e-commerce usage, especially notable for its high accuracy rate. The study also suggests strategies for improving e-commerce usage.

Anahtar Kelimeler

E-commerce, Household Information Technologies, Machine Learning, Classification Techniques, Data Mining

Kaynakça

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