AN INTEGRATED MACHINE LEARNING BASED DECISION SUPPORT MODEL FOR PREDICTION OF BANK TELEMARKETING SUCCESS

Yıl 2022, Sayı: 1, 94 - 109, 31.01.2022

Ömer Algorabi Ersin Namlı

https://doi.org/10.51551/verimlilik.748616

Cited By: 1

Öz

Purpose: Today, electronic banking, which enables the capture of transaction data, has started to be adopted more and the amount of such data has increased significantly. Data mining-based techniques have been adopted to analyze this data. In this study, it is aimed to classify customers according to their time deposit eligibility status. The bank usually needs to make more than one phone connection to the same customer to understand whether a time deposit product can be offered.

Methodology: In this study, the data set used consists of the marketing campaigns data obtained by the Portuguese Banking Agency from its customers via telephone communication. Data is classified with C4.5, Naive Bayes, Bayes Networks, k-Nearest Neighbor and Sequential Minimal Optimization (SMO) classification algorithms. Classification models are compared according to synthesis index (SI) values.

Findings: According to the results, simple C4.5 was found to be the best classification model. The proposed model was found to be superior to the methods applied by other studies in the literature on the same data set.

Originality: Different from the existing studies in the literature, in this study, different classification models were created with ensemble learning methods and a new performance criterion was developed as a synthesis index.

Anahtar Kelimeler

Banking, Telemarketing, Machine Learning, Classification

BANKA TELEPAZARLAMA BAŞARISININ TAHMİNİ İÇİN BİR BİRLEŞİK MAKİNE ÖĞRENME TABANLI KARAR DESTEK MODELİ

Öz

Amaç: Günümüzde bankacılık sektöründeişlem verilerinin yakalanmasını sağlayan elektronik bankacılık daha çok benimsenmeye başlanmış ve bu tür verilerin miktarı önemli ölçüde artmıştır. Bu verileri analiz etmek için veri madenciliğine dayalı teknikler benimsenmiştir. Bu çalışmada müşterilerin vadeli mevduat uygunluk durumlarına göre sınıflandırılması amaçlanmıştır.

Yöntem: Bu çalışmada, kullanılan veri seti Portekiz Bankacılık Kurumu'nun müşterilerinden telefon ile iletişim yoluyla elde ettiği pazarlama kampanyaları verilerinden oluşmaktadır. Veriler C4.5, Naive Bayes, Bayes Ağları, k-En Yakın Komşu ve Sıralı Minimal Optimizasyon (SMO) sınıflandırma algoritmaları kullanılarak sınıflandırılmıştır. Sınıflandırma modelleri Sentez indeks (SI) değerlerine göre karşılaştırılmıştır.

Bulgular: Elde edilen sonuçlara göre basit C4.5, en iyi sınıflandırma modeli olarak bulunmuştur. Önerilen model, literatürdeki diğer çalışmaların aynı veri seti üzerinde uyguladığı yöntemlerden daha üstün bulunmuştur.

Özgünlük: Literatürdeki mevcut çalışmalardan farklı olarak bu çalışmada, topluluk öğrenme yöntemleri ile farklı sınıflandırma modelleri oluşturulmuş ve sentez indeks olarak yeni bir performans ölçütü geliştirilmiştir.

Anahtar Kelimeler

Bankacılık, Tele Pazarlama, Makine Öğrenme, Sınıflandırma

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