Telecom Customer Churn Prediction using enhanced Machine Learning Classification Techniques

For telecommunications corporations, customer attrition is a chief trouble that outcomes in large revenue losses and better costs of customer acquisition. by using putting in place cantered retention programmes, corporations who can as it should be forecast attrition can reduce these losses. in this work, improved device learning classification methods are used to predict customer attrition inside the telecom sector. Three classifiers were used to build predictive models: Decision Tree, Random Forest, and K-Nearest Neighbours (KNN). The models produced, in turn, accuracy of 79%, 82%, and 79%. The Random Forest classifier proved to be the only approach for forecasting viable client churn due to its superior performance in extra correctly predicting such turnover. This work gives a reliable approach for predicting consumer turnover by the usage of machine gaining knowledge of classifiers like selection Tree, Random Forest, and KNN. because those models are so accurate—the Random wooded area specifically—telecom corporations may additionally better manage client happiness, reduce churn risks, and maximise retention efforts.