Customer Churn Prediction in Telecom Industry Using Regression Algorithms
DOI:
https://doi.org/10.55524/Keywords:
Machine Learning, Logistic Regression, Churn Prediction, Feature Engineering, and Accuracy ScoreAbstract
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.
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