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Evaluating the Impact of PCA-Based Feature Extraction on Predicting Customer Attrition in the Banking Sector

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
N. Siyad
Sunu Mary Abraham
Ann Baby
Jaya Vijayan
Published in
Computer Vision and Robotics
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the evaluation of PCA-based feature extraction's impact on predicting customer attrition in the banking sector. The research employs advanced feature engineering and machine learning techniques, focusing on dimensionality reduction, clustering, and model refinement. Key topics include the methodology of integrating unsupervised and supervised learning, the application of Mixed Variable PCA for dimensionality reduction, and the performance comparison of Random Forest, SVM, and XGBoost models. The study highlights the superior performance of XGBoost in identifying attrited customers, offering a framework for developing effective data-driven customer retention strategies. The results demonstrate that feature extraction enhances XGBoost's ability to detect attrition, making it the preferred model for balancing sensitivity and specificity. The research also discusses the limitations and future works, suggesting the potential of advanced techniques like deep learning and hybrid models to further improve performance.
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previous chapter SegCSA-X: A Robust Segmentation Model for Palmprint Images Under Illuminated Challenges
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Title
Evaluating the Impact of PCA-Based Feature Extraction on Predicting Customer Attrition in the Banking Sector
Authors
N. Siyad
Sunu Mary Abraham
Ann Baby
Jaya Vijayan
Copyright Year
2026
Book
Computer Vision and Robotics

Print ISBN: 978-3-032-06252-9

Electronic ISBN: 978-3-032-06253-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-06253-6_35
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