Predicting of Credit Card Customer Churn Using Machine Learning Methods

Abstract

Today, customers are giving up using credit cards for various reasons and this has negative consequences for banks. Therefore, it is necessary to predict potential customers who will cancel their credit cards in advance and to turn these cancellations in favor of the bank and thus to regain the customers. This situation is also very important in terms of monitoring customer loss and preventing such loss. In this context, a model is proposed using machine learning methods to detect the card cancellation status of customers using credit cards and thus predict customer loss. A dataset obtained from the Kaggle platform was utilized to create the model. This dataset contains credit card data belonging to a total of 10127 customers. Although there were 23 features in the dataset, 2 features were deleted without being included in the model because they did not affect the results. As a result, a total of 21 different variables were used, 20 inputs and 1 output. The models were created using Artificial Neural Networks, Logistic Regression, Support Vector Machines, K-Nearest Neighbor, Decision Tree, Random Forest, Ada Boost, and Gradient Boosting machine learning algorithms. As a result, it was seen that the model with the highest performance was Gradient Boosting with a rate of 98.70%, and the model with the lowest performance was Support Vector Machines with a rate of 67.9%. All these results clearly show that Credit Card Customer Churn can be effectively predicted by machine learning methods.

Keywords

• Credit Card
• Card Cancellation
• Machine Learning
• Prediction
• Churn costumer

Kredi Kartı Müşteri Kaybının Makine Öğrenmesi Yöntemleri Kullanılarak Tahmin Edilmesi

Abstract

Günümüzde müşteriler muhtelif sebeplerle kredi kart kullanımlarından vazgeçebilmekte ve bu durum bankalar açısından olumsuz sonuçlar doğurmaktadır. Dolayısıyla, kredi kartı iptali yapacak muhtemel müşteriler önceden tahmin edilerek sözkonusu iptallerin banka lehine çevrilmesi ve böylece müşterilerin geri kazanılması gerekmektedir. Bu durum, müşteri kaybının takibi ve sözkonusu kaybın önlenmesi açısından da oldukça önem arzetmektedir. Bu bağlamda, çalışmada kredi kartı kullanan müşterilerin kart iptal durumlarını tespit etmek ve dolayısıyla müşteri kaybını tahmin etmek için makine öğrenmesi yöntemleri kullanılarak bir model önerilmiştir. Modelin oluşturulması için Kaggle platformundan elde edilen bir verisetinden yararlanılmıştır. Bu veri setinde toplam 10127 müşteriye ait kredi kart verisi bulunmaktadır. Veri setinde 23 özellik olmasına rağmen 2 özelliğin sonuçlara etkisi olmadığından modele dahil edilmeden silinmiştir. Sonuç olarak 20 girdi, 1 çıktı olmak üzere toplamda 21 farklı değişken kullanılmıştır. Modeller; Yapay Sinir Ağları, Lojistik Regresyon, Destek Vektör Makineleri, K-En Yakın Komşu, Karar Ağacı, Rastgele Orman, Ada Boost ve Gradient Boosting makine öğrenmesi algoritmaları kullanılarak oluşturulmuştur. Sonuç olarak, en yüksek performansı gösteren modelin %98.70’lik bir oranla Gradient Boosting, en düşük performansın ise %67.9’luk bir oranla Destek Vektör Makineleri modeli olduğu görülmüştür. Elde edilen tüm bu sonuçlar, Kredi Kartı Müşteri Kaybının makine öğrenmesi yöntemleriyle etkili bir şekilde tahmin edilebileceğini açıkça göstermektedir.

Keywords

• Kredi Kartı
• Müşteri Kaybı
• Kart İptali
• Makine Öğrenmesi
• Tahmin

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