İşsizlik Oranının Tahmininde Makine Öğrenimi Algoritmalarının Performansının İncelenmesi

Abstract

İşsizlik, tüm ülkeyi derinden etkiliyor. Bu, kişisel bir mücadeleden çok daha fazlasıdır. İşsizlik oranı, ekonominin sağlığının önemli bir göstergesidir. Hükümetler bu konuya yakından odaklanmaktadır. Düşük işsizlik oranı, ekonominin iyi işlediğini gösterir. Bu durum, herkes için daha iyi bir yaşamı destekler. Birçok ülke, yeni iş olanakları yaratmak için destek sunmaktadır. Bu araştırma, Türkiye’deki gelecekteki işsizlik oranını tahmin etmektedir. Araştırmada RBF Regresyonu, SVM Regresyonu ve Rastgele Orman yöntemleri kullanılmaktadır. Çalışma, 2005 ile 2025 yılları arasındaki verileri incelemektedir. Her bir algoritma, diğerleriyle karşılaştırılarak test edilmektedir. Bunlar arasında SVM Regresyonu öne çıkmaktadır. Bu yöntem, %91 gibi yüksek bir doğruluk oranına ulaşmaktadır. Çalışmada ayrıca, önemli faktörleri belirlemek için ReliefF algoritması kullanılmaktadır. Sanayi ve inşaat sektörleri en önemli faktörler olarak öne çıkmaktadır. Bunu istihdam ve işgücüne katılım oranları izlemektedir. Sonuçlar, daha az değişken kullanıldığında bile sağlam kalmaktadır. Bu durum, basit bir modelin de etkili bir şekilde işlediğini kanıtlamaktadır. Başarılı bir tahmin için yalnızca en ilgili veriler gereklidir.

Keywords

• İşsizlik oranı
• Tahmin
• Makine öğrenmesi
• Öznitelik seçimi

Supporting Institution

Çalışmada herhangi bir kurum ya da kuruluştan destek alınmamıştır.

Ethical Statement

Etik kurul belgesi gerektiren bir çalışma değildir.

Examining the Performance of Machine Learning Algorithms in Estimating the Unemployment Rate

Abstract

Unemployment affects the whole country deeply. It is more than a personal struggle. This rate serves as a key sign of economic health. Governments focus on this issue closely. Low unemployment means the economy works well. It supports a better life for everyone. Many countries offer help to create new jobs. This research predicts future unemployment in Türkiye. It applies RBF Regression, SVM Regression, and Random Forest methods. The study examines data between 2005 and 2025. It tests each algorithm against the others. SVM Regression stands out among them. It reaches a high accuracy of 91%. The study also uses the ReliefF algorithm to pick important factors. Industry and construction sectors matter the most. Employment and labor force participation rates follow them. The results stay strong even with fewer variables. This proves that a simple model works effectively. Successful forecasting requires only the most relevant data.

Keywords

• Unemployment rate
• Estimation
• Machine learning
• Feature selection

Supporting Institution

No support is taken from any institution or organization.

Ethical Statement

The study does not necessitate an approval of ethical committee.

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