Customer Churn Prediction in Telecom Industry Using Regression Algorithms (International Journal of Innovative Research in Computer Science & Technology, 2022)
Customer acquisition and retention is a  major challenge in a variety of industries, but it is most  severe in highly competitive and fast-growing companies.  Customer turnover is a major worry for large organisations  since keeping a loyal customer is significantly more  valuable than gaining a new one. Finding the causes that  cause customer turnover is critical for implementing the  appropriate solutions to prevent and reduce churn. The goal  of this study is to employ machine learning (ML)  algorithms to detect prospective churn clients, categorise  them based on usage patterns, and illustrate the findings of  the analysis. Extra Trees Classifier, XGBoosting Algorithm,  and Decision Tree, Random Forest have the best churn  modelling performance, especially for 80:20 dataset  distribution, with AUC scores of 0.85, 0.96, and 0.977,  respectively.
Keywords: Machine Learning; Logistic Regression; Churn Prediction; Feature Engineering; and Accuracy Score