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Estimating customer traffic is an important task for businesses because it helps them identify customers who are most likely to leave and take preventative measures to retain them by improving customer satisfaction and further increasing their own revenue. In this article, we focus on developing a machine-learning model for predicting customer churn using historical customer data We performed engineering operations on the data, addressed the missing digits, coded the categorical variables, and preprocessed the data before evaluating it using a variety of performance indicators, including accuracy, precision, recall, f1 score, and ROC AUC_Score. Our feature significance analysis revealed that monthly fees, customer tenure, contract type, and payment method are the factors that have the most impact on forecasting customer churn. Finally, we conclude the best-performing model, the Soft Voting Classifier, implemented on the four best-performing classifiers with a good accuracy of 0.78 and a relatively better ROC AUC_Score of 0.82.