Projection of Customer Churn in Telecom Sector through Machine Learning Algorithms on Big Data Platforms

K. Sasidhar, Arvind K Sharma and Rajesh Kulkarni

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 6, 975-988

Abstract: The telecom industry study is essential for increasing the profitability of enterprises, especially through precise churn prediction. The goal of this study was to create a specialized churn prediction system for the telecom provider SyriaTel. For accurate churn estimates, high AUC values were necessary, and the dataset was divided into 30% testing and 70% training sets. Hyperparameter adjustment and accurate model evaluation were made possible via cross-validation. To get the features ready for machine learning algorithms, feature engineering and selection techniques were used. Tree-based methods and under-sampling were used to address data imbalance. Decision Tree, Random Forest, Gradient Boosting Machine, and XGBOOST are the four tree-based models that were selected. Strategic planning and the incorporation of mobile social network features were essential to success. With a 93.301% AUC on the SyriaTel dataset, XGBOOST performed better than GBM, Random Forest, and Decision Tree. When tested on a fresh dataset, XGBOOST's AUC was 89%. Non-stationary data necessitates frequent model retraining. Social network analysis was used to improve telecom churn prediction.

Keywords: Boosting; Feature Engineering; Selection Techniques; Churn; Fuzzy Rules; Dataset (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i6:id:585

DOI: 10.32628/IJSRST2512140

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