Performance Comparison of Boosting Methods on Customer Churn Data

Başak Ceren Seçik Göçer; İbrahim Emiroğlu

Makale detayları

Performance Comparison of Boosting Methods on Customer Churn Data

Yazarlar:

Başak Ceren Seçik Göçer | İbrahim Emiroğlu

Yayıncı:

Cankiri Karatekin University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Alpha Journal of Engineering and Applied Sciences Vol.2 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001309308

pISSN:

3023-6002

Anahtar kelimeler:

Özet:

 Data mining and machine learning models are frequently used today to generate insights in churn analysis. Through churn analysis, businesses can make inferences before their customers leave the company or stop using their products, and can increase both profits and customer satisfaction by reducing customer churn. There are many ways to perform these analyses. Rule-based models can be developed and predictions can be made with various machine learning models. In this article, machine learning models were built and analyzed on a data set consisting of 7043 observations and 57 variables, which is publicly available on kaggle.com. As a result of this analysis using customers data, it was predicted which customers will continue to be customers of the telecom company and which customers will leave the company. It is discussed which features are important on the churn situation. Light GBM, XGBoost, CatBoost, and Gradient Boosting methods are used as machine learning models and the performances between these boosting methods are evaluated. The data set is balanced by applying resampling techniques. Model performance was assessed based on accuracy, F1-Score, and sensitivity metrics. When metrics accuracy and F1-Score were evaluated, no significant difference was found in the model performances. However, when metric sensitivity was assessed, the best performance was achieved by the Extreme Gradient Boost XGBoost model with a ratio of 0.949.

 Data mining and machine learning models are frequently used today to generate insights in churn analysis. Through churn analysis, businesses can make inferences before their customers leave the company or stop using their products, and can increase both profits and customer satisfaction by reducing customer churn. There are many ways to perform these analyses. Rule-based models can be developed and predictions can be made with various machine learning models. In this article, machine learning models were built and analyzed on a data set consisting of 7043 observations and 57 variables, which is publicly available on kaggle.com. As a result of this analysis using customers data, it was predicted which customers will continue to be customers of the telecom company and which customers will leave the company. It is discussed which features are important on the churn situation. Light GBM, XGBoost, CatBoost, and Gradient Boosting methods are used as machine learning models and the performances between these boosting methods are evaluated. The data set is balanced by applying resampling techniques. Model performance was assessed based on accuracy, F1-Score, and sensitivity metrics. When metrics accuracy and F1-Score were evaluated, no significant difference was found in the model performances. However, when metric sensitivity was assessed, the best performance was achieved by the Extreme Gradient Boost XGBoost model with a ratio of 0.949.

 Data mining and machine learning models are frequently used today to generate insights in churn analysis. Through churn analysis, businesses can make inferences before their customers leave the company or stop using their products, and can increase both profits and customer satisfaction by reducing customer churn. There are many ways to perform these analyses. Rule-based models can be developed and predictions can be made with various machine learning models. In this article, machine learning models were built and analyzed on a data set consisting of 7043 observations and 57 variables, which is publicly available on kaggle.com. As a result of this analysis using customers data, it was predicted which customers will continue to be customers of the telecom company and which customers will leave the company. It is discussed which features are important on the churn situation. Light GBM, XGBoost, CatBoost, and Gradient Boosting methods are used as machine learning models and the performances between these boosting methods are evaluated. The data set is balanced by applying resampling techniques. Model performance was assessed based on accuracy, F1-Score, and sensitivity metrics. When metrics accuracy and F1-Score were evaluated, no significant difference was found in the model performances. However, when metric sensitivity was assessed, the best performance was achieved by the Extreme Gradient Boost XGBoost model with a ratio of 0.949.

 Müşteri kaybı analizinde öngörü oluşturmak için günümüzde veri madenciliği ve makine öğrenmesi modelleri sıklıkla kullanılmaktadır. Müşteri kaybı analizi sayesinde, işletmeler müşterileri şirketi terk etmeden veya ürünlerini kullanmayı bırakmadan önce bazı çıkarımlarda bulunabilir ve müşteri terk oranını düşürerek hem kârlarını hem de müşteri memnuniyetini artırabilir. Bu analizleri yapmanın birçok yolu bulunmaktadır. Kural tabanlı modeller geliştirilebilir, çeşitli makine öğrenmesi modelleriyle tahminler yapılabilir. Bu makale çalışmasında kaggle.com sitesinde açık erişime sunulan 7043 gözlem 57 değişkenden oluşan veri seti üzerinde makine öğrenmesi modelleri kurularak analizler gerçekleştirilmiştir. Müşterilerin verileri kullanılarak yapılan bu analiz sonucunda hangi müşterinin Telekom şirketinin müşterisi olarak kalmaya devam edeceği, hangi müşterinin şirketi terk edeceği tahmin edilmiştir. Terk etme durumu üzerinde hangi özelliklerin önemli olduğu tartışılmıştır. Makine öğrenmesi modelleri olarak Hafif Gradyan Artırma Light GBM, Aşırı Gradyan Artırma XGBoost, CatBoost ve Gradyan Artırma Gradient Boosting metotları kullanılmış ve bu artırma metotları arasındaki performanslar değerlendirilmiştir. Veri seti yeniden örnekleme teknikleri uygulanarak veri dengeli hale getirilmiştir. Doğruluk, F1 - Skoru ve duyarlılık metrikleri baz alınarak model başarıları ölçülmüştür. Doğruluk ve F1- Skoru metrikleri değerlendirildiğinde model performansları arasında anlamlı fark bulunmazken, duyarlılık metriği değerlendirildiğinde en iyi performans Aşırı Gradyan Artırma XGBoost modeli tarafından 0,949 oranıyla sağlanmıştır.

Makale detayları

Performance Comparison of Boosting Methods on Customer Churn Data

Yazarlar:

Başak Ceren Seçik Göçer | İbrahim Emiroğlu

Yayıncı:

Cankiri Karatekin University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Alpha Journal of Engineering and Applied Sciences Vol.2 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001309308

pISSN:

3023-6002

Customer churn analysis

telecommunication

classification

machine learningMüşteri kayıp analizi

telekomünikasyon

sınıflandırma

makine öğrenmesi

Sayı

Makale detayları

Yazarlar: Başak Ceren Seçik Göçer | İbrahim Emiroğlu

Yayıncı: Cankiri Karatekin University

Ana Başlıklar: Software Engineering (Other)

Dergi: Alpha Journal of Engineering and Applied Sciences Vol.2 Issue 1, pp.1-16

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001309308

pISSN: 3023-6002

Sayı

Yazarlar:

Başak Ceren Seçik Göçer | İbrahim Emiroğlu

Yayıncı:

Cankiri Karatekin University

Ana Başlıklar:

Software Engineering (Other)

Dergi:

Alpha Journal of Engineering and Applied Sciences Vol.2 Issue 1, pp.1-16

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001309308

pISSN:

3023-6002