Customer churn prediction in telecom using machine learning

  • Authors

    • Ahmed Faraz Department of Telecommunication Engineering BUITEMS
    • Sibghat Ullah Bazai Department of Computer Engineering BUITEMS
    • Shumaila Hussain Department of Computer Science SBKWU
    • Hamayoun Yousaf Shahwani Department of Telecommunication Engineering BUITEMS
    • Mehmood Baryalai Department of Computer Science BUITEMS
    • Shariqa Fakhar Department of Computer Science SBKWU
    2024-11-07
    https://doi.org/10.14419/73k1zb75
  • Customer churn prediction, Telecommunication industry, Machine learning algorithms, Feature extraction, Imbalanced data

  • Abstract

    The customer churn prediction has become crucial for retaining customers in rapidly growing telecommunication industry. It enables telecommunication industry to avoid sizeable revenue losses. There are many techniques available to analyze the customer churn. It is still a challenging task due to large data size, imbalanced class distribution, and high dimensionality of telecom datasets.

    This study contributes to formalize customer churn prediction. The study investigates the most promising machine learning algorithms XGboost and Logistic Regression. Furthermore; the feature extraction will be used before implementing the classification algorithms in order to analyze the impact.

     

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  • How to Cite

    Faraz, A., Ullah Bazai, S., Hussain, S., Shahwani, H. Y., Baryalai, M., & Fakhar, S. (2024). Customer churn prediction in telecom using machine learning. Journal of Advanced Computer Science & Technology (JACST), 12(2), 60-66. https://doi.org/10.14419/73k1zb75

    Received date: 2022-08-14

    Accepted date: 2022-10-04

    Published date: 2024-11-07