Abstract:
The research inspects the application of machine learning approaches to forecasting
customer attrition in telecommunication companies. Machine learning
models such as extreme gradient boosting, random forest, k-nearest neighbour,
adaptive boosting, support vector machine, and logistic regression were
used to forecast and compared the best model and analysed churn behaviour.
Cross-validation techniques were applied to enhance model performance, revealing
critical predictors of churn such as contract length, customer tenure,
and service usage patterns. The results emphasised the effectiveness of machine
learning in accurately identifying potential churners. Furthermore, the
study emphasises the importance of leveraging predictive analytics to proactively
address customer attrition, enabling telecommunication companies to
devise targeted retention strategies and enhance customer satisfaction and loyalty.
