Answer
Supervised learning techniques such as logistic regression, decision trees, random forests, and support vector machines are commonly employed to classify customers as potential churners or loyal customers based on historical data. Logistic regression, random forest, and gradient boosting machines are effective machine learning models for predicting customer churn.
Gradient boosting machines (GBM) are a powerful tool in churn prediction where trees are built sequentially, with each new tree aiming to correct the errors of its predecessor, yielding highly accurate models through gradual refinement. Using a Random Forest classifier on a publicly available telecom dataset with 2,668 records, one model achieved 95.13% accuracy and an AUC of 0.89, with techniques such as SMOTE and class weighting applied to address class imbalance. Feature importance analysis revealed that total day minutes, total day charge, and customer service calls were the most influential predictors.