Churn Prediction: A Predictive Modeling Approach
Keywords:
Churn Production, Churn Modeling, Productive Modeling, Predictive Modeling, Customer ChurnAbstract
Customer churn, the phenomenon of customers discontinuing their service or subscription, presents a significant challenge for businesses across various industries. Accurately predicting churn is crucial for proactive retention strategies and maximizing customer lifetime value. This research paper explores various predictive modeling techniques for churn prediction, including Logistic Regression, Support Vector Machines, Random Forest, and Neural Networks. We evaluate their performance on a publicly available telecom churn dataset, comparing metrics such as accuracy, precision, recall, F1- score, and AUC-ROC. The study aims to identify the most effective model for churn prediction and discuss the key factors contributing to customer churn, offering actionable insights for businesses.
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