Prediction of Customer Attrition Using Feature Extraction Techniques and Its Performance Assessment Through Dissimilar Classifiers

  • R. SugunaEmail author
  • M. Shyamala Devi
  • P. Praveen Kumar
  • P. Naresh
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Dimensionality reduction is the process of identifying insignificant data variables and dropping them. The process culminates in obtaining a set of principal variables. Dimensionality reduction not only removes the redundant features, also reduces storage and computation time. Feature Selection and Feature extraction are the two components in dimensionality reduction. This paper explores techniques used for feature extraction and analyzes the results by applying the techniques to customer churn dataset. The performance of these techniques in different classifiers is also compared and results are visualized in graphs.


Machine learning Dimensionality reduction Feature extraction PCA Factor analysis Singular Value Decomposition (SVD) ICA 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • R. Suguna
    • 1
    Email author
  • M. Shyamala Devi
    • 1
  • P. Praveen Kumar
    • 2
  • P. Naresh
    • 3
  1. 1.Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyChennaiIndia
  2. 2.Vignana Bharathi Institute of TechnologyHyderabadIndia
  3. 3.Sri Indu College of Engineering and TechnologyHyderabadIndia

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