<![CDATA[Customer churn represents one of the most critical challenges in the telecommunications industry, as the cost of acquiring new customers significantly outweighs the expense of retaining existing ones. High churn rates directly impact corporate revenue stability and market competitiveness, necessitating the development of precise predictive systems. This study presents a comprehensive comparative analysis of two prominent ensemble learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to establish a robust predictive framework for identifying potential churners using a large-scale Telco subscriber dataset. To ensure the reliability and scientific validity of the comparison, the research methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to rigorously address the inherent class imbalance within the dataset, ensuring that the minority churn class is adequately represented during the training phase to avoid model bias. Furthermore, a systematic hyperparameter tuning process was executed via GridSearchCV, exploring multiple combinations of estimators, depth, and learning rates to identify the optimal configurations for both algorithms. The experimental results reveal that while both models are highly effective, Random Forest slightly outperformed XGBoost, achieving an overall accuracy of 77.54% and a balanced F1-score of 0.616, compared to XGBoost’s accuracy of 76.54% and F1-score of 0.605. Notably, although both models demonstrated an identical recall rate of 67.64%, Random Forest exhibited superior precision (56.47% vs. 54.76%), which is vital for minimizing false positives and ensuring cost-effective retention campaigns. Feature importance analysis, conducted through Gini impurity and gain metrics, further identified tenure, total charges, and month-to-month contract types as the primary drivers of customer attrition. This study concludes that an optimized Random Forest model provides a more stable and accurate framework for telecommunication providers to proactively mitigate customer turnover. The findings offer valuable business intelligence, allowing stakeholders to transition from reactive measures to proactive, data-driven loyalty programs that enhance long-term business sustainability.]]>
