Identifying customer churn in Telecom sector: A Machine Learning Approach

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Comparative analysis of ML models shows random forest outperforms SVM and decision tree in predicting customer churn.

Random forest model achieved a remarkable 94% accuracy in identifying customer churn.
Model validation was performed using a ten-fold cross-validation approach to ensure reliability.
Feature selection utilized chi-square to enhance model performance by selecting the most informative features.
Proposed methodology outperformed existing models on the same telecommunications dataset.

Hambali et al. (2024) studied this question.

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View Full Paper

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Authors

Comparative analysis of ML models shows random forest outperforms SVM and decision tree in predicting customer churn.

Random forest model achieved a remarkable 94% accuracy in identifying customer churn.
Model validation was performed using a ten-fold cross-validation approach to ensure reliability.
Feature selection utilized chi-square to enhance model performance by selecting the most informative features.
Proposed methodology outperformed existing models on the same telecommunications dataset.

Hambali et al. (2024) studied this question.

View Full Paper

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Bookmark