A Comparative Study of Feature Selection Techniques for Customer Churn Prediction

Merit Chinonso Opara; Benjamin Chiemeka Opara; Uchechi Joyce Nneji; Chibueze; Favour Aririguzo; Confidence Chigozirim Olumba; Chidimma Grace Emmanuel; Prisca Chimezie Opara; Happy Nkanta Monday

doi:10.56201/ijebm.vol.12.no2.2026.pg142.179

A Comparative Study of Feature Selection Techniques for Customer Churn Prediction

Merit Chinonso Opara, Benjamin Chiemeka Opara, Uchechi Joyce Nneji, Chibueze, Favour Aririguzo, Confidence Chigozirim Olumba, Chidimma Grace Emmanuel, Prisca Chimezie Opara, Happy Nkanta Monday

Abstract

This paper investigates the effectiveness of feature selection techniques in optimizing supervised machine learning pipelines for customer churn prediction using the publicly available Customer Churn Dataset from Kaggle. Feature selection plays a crucial role in enhancing model interpretability and generalization by eliminating irrelevant or redundant variables. In this study, three optimized feature selection algorithms—Minimum Redundancy-Maximum Relevance , Multi Spatially Uniform Relief (MultiSURF), and Hilbert-Schmidt Independence Criterion —were implemented and compared alongside a baseline scenario without feature selection. Eight supervised machine learning classifiers, including Support Vector Machine , Logistic Regression, K-Nearest Neighbors , Decision Tree, AdaBoost, Bagging, Stacking, and Voting Classifier, were optimized using Randomized GridSearchCV. The dataset was divided into training and testing subsets with a 70:30 ratio, and model performance was evaluated using metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and computational time. Experimental results reveal that all feature selection techniques achieved comparable performance across classifiers. This study concludes that optimized feature selection techniques contribute to more stable and efficient model training. P-ISSN 2695-186X

Keywords

Customer Churn Prediction; Feature Selection; Supervised Machine Learning; mRMR; MultiSURF; Hilbert–Schmidt Independence Criterion ; Ensemble Classifiers; Randomized GridSearchCV

References

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A Comparative Study of Feature Selection Techniques for Customer Churn Prediction

Abstract

Keywords

References

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