Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost

Ako ; Rita Erhovwo ; Aghware ; Fidelis Obukohwo ; Okpor ; Margaret Dumebi ; Akazue ; Maureen Ifeanyi ; Yoro ; Rume Elizabeth ; Ojugo ; Arnold Adimabua ; Setiadi ; De Rosal Ignatius Moses ; Odiakaose ; Chris Chukwufunaya ; Abere ; Reuben Akporube ; Emordi ; Frances Uche ; Geteloma ; Victor Ochuko ; Ejeh ; Patrick Ogholuwarami

Journal of Computing Theories and Applications 📅 2024 Vol 2, No 1 🔗 DOI: 10.62411/jcta.10562

Abstract

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

Keywords

Customer attrition; Churn; Imbalanced dataset; Random Forest; XGBoost; SMOTE; Random-Under-Sampling; SMOTEEN

How to Cite

Ako, et al. (2024). Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost. Journal of Computing Theories and Applications, 2(1). https://doi.org/10.62411/jcta.10562

Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami, "Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost," Journal of Computing Theories and Applications, vol. 2, no. 1, 2024.

Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami. "Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost." Journal of Computing Theories and Applications, vol. 2, no. 1, 2024.

Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami. "Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost." Journal of Computing Theories and Applications 2, no. 1 (2024).

Ako, et al. (2024) 'Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost', Journal of Computing Theories and Applications, 2(1). doi: 10.62411/jcta.10562.

Ako, Rita Erhovwo; Aghware, Fidelis Obukohwo; Okpor, Margaret Dumebi; Akazue, Maureen Ifeanyi; Yoro, Rume Elizabeth; Ojugo, Arnold Adimabua; Setiadi, De Rosal Ignatius Moses; Odiakaose, Chris Chukwufunaya; Abere, Reuben Akporube; Emordi, Frances Uche; Geteloma, Victor Ochuko; Ejeh, Patrick Ogholuwarami. Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost. Journal of Computing Theories and Applications. 2024;2(1).

An Attention-Enhanced CNN–RBF Framework for Network Intrusion Detection in Imbalanced Traffic

Kabura, Fabrice; Nsabimana, Thierry

A Lightweight Maize Leaf Disease Recognition Using PCA-Compressed MobileNetV2 Features and RBF-SVM

Abubakar, Mustapha; Ibrahim, Yusuf; Ajayi, Ore-Ofe; Saminu, Sani Saleh

Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework

Ibam, Emmanuel Onwako; Oluwagbemi, Johnson Bisi

Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing

Masari, Maryam Sufiyanu; Danladi, Maiauduga Abdullahi; Onyinye, Ilori Loretta; Tohomdet, Loreta Katok

Hybrid Real-time Framework for Detecting Adaptive Prompt Injection Attacks in Large Language Models

Prakash, Chandra; Lind, Mary; De La Cruz, Elyson

Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation

Qureshi, UmmeAmmara; Doshi, Bhumika; More, Aditya; Joshi, Kashyap; Kumar, Kapil

Tren Sitasi Jurnal