Makine Öğrenmesi Teknikleri Kullanılarak Bankalardaki Potansiyel Müşterilerin Sınıflandırılması

Abstract

Bu çalışmada, bankalar için telefon görüşmeleri baz alınarak potansiyel müşterilerin sınıflandırılması hedeflenmiştir. Öncelikle problem dikkatli bir şekilde ele alınmış ve bu alandaki literatür taranmıştır. Daha sonrasında daha iyi ve daha hızlı bir çözüm ortaya konması istenmiştir. Bu yüzden bir dizi iyi bilinen sınıflandırma algoritmaları kullanılacak veri setine uygulanmış ve karşılattırmalı bir çalışma gerçekleştirilmiştir. Sonuçlar ışığında en iyi sonucu veren iki model %95,1 ile XGBoost modeli ve %94,6 ile Random Forest modeli olmuştur. Daha sonrasında ise daha iyi sonuçlar alınabilmesi için farklı hiperparametre optimizasyonu yapan metotlar denenmiştir. Ancak Random Forest modelinde çok düşük bir artış gözlemlense de hiperparametre optimizasyonu işlemden sonra çok büyük bir artışı kayıt edilmemiştir.

Keywords

• Makine öğrenmesi
• banka mevduat hesabı
• sınıflandırma
• hiper parametre optimizasyonu

Classification of Potential Customers in Banks Using Machine Learning Techniques

Abstract

In this study, it is aimed to classify potential customers based on phone calls for banks. First of all, the problem was handled carefully and the literature in this field has been investigated. Afterward, it is observed that it is required to come up with a better and faster solution. Therefore, a number of well-known classification algorithms have been applied to the data set and a comparison study has been conducted. In the light of the results, the two models that gave the best results were the XGBoost model with 95.1% and the Random Forest model with 94.6%. Afterwards, different hyperparameter optimization methods were tried to obtain better results. However, although a very low increase was observed in the Random Forest model, a very large increase was not recorded after the hyperparameter optimization process.

Keywords

• Makine öğrenmesi
• banka mevduat hesabı
• sınıflandırma
• hiper parametre optimizasyonu

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