Performance Analysis of Customer Attrition Trend in Telecommunication Sectors using Machine Learning and Deep Learning

Customer churn poses a significant challenge to telecommunication sectors, and making correct predictions is crucial for effective resource allocation and profitability of the company. Finding new customers is a more challenging task than retaining the old ones, highlighting the importance of proactive churn management. This research work investigates machine learning algorithms to analyze performances of different algorithms. We leverage 8 widely used supervised algorithms for classification. These are Adaboost, XGBoost and LightGBM gradient boosting techniques, Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Machines(SVM), Naïve Bayes(NB), Logistic Regression(LR), and Deep learning to build predictive models capable of identifying customers’ churn. Each model is trained using a telecom dataset encompassing various customer behavioral and demographic features. The assessment of the models is done using a confusion matrix to compute accuracy scores, allowing for a comparative analysis of the algorithms' effectiveness. The results provide ideas about advantages and limitations of the algorithms in telecommunication sector churn, informing the selection of optimal models for practical deployment and contributing to the application of more efficient customer retention strategies to take measures by the company beforehand. This research is used to enhance customer relationship management in the telecommunications sector.