Dublin Core
Title
PREDICTING CUSTOMER CHURN USING MACHINE LEARNING: A DATA-DRIVEN APPROACH FOR RETENTION STRATEGIES
Abstract
Customer churn poses a critical challenge to business sustainability, leading to significant revenue loss and signaling potential issues in customer satisfaction. This project addresses this by developing a robust machine learning framework to accurately predict customers at high risk of discontinuing service, equipping businesses with a data-driven tool for proactive retention. The methodology encompasses the entire data science lifecycle, beginning with the preprocessing of the Telco Customer Churn dataset, where advanced feature engineering and the Synthetic Minority Oversampling Technique (SMOTE) were applied to enhance data quality and address class imbalance. A diverse portfolio of classification algorithms—including logistic regression, random forests, and gradient boosting—was trained and rigorously evaluated using metrics such as accuracy, precision, recall, and AUC-ROC. To maximize predictive power, a soft-voting ensemble was constructed from the top-performing models.
The final ensemble achieved an overall accuracy of 76.8% and, critically, a recall of 73% for the churn class, demonstrating strong reliability in identifying at-risk individuals. A key deliverable is an interactive user interface allowing stakeholders to leverage model predictions, visualize churn trends, and interpret the key factors driving customer behavior. This study culminates in a predictive tool that provides actionable insights, enabling businesses to transition from a reactive to a proactive approach in managing customer relationships. By identifying at-risk customers, companies can deploy targeted interventions to enhance loyalty, reduce churn, and secure long-term profitability, creating a practical bridge between advanced analytics and strategic decision-making
The final ensemble achieved an overall accuracy of 76.8% and, critically, a recall of 73% for the churn class, demonstrating strong reliability in identifying at-risk individuals. A key deliverable is an interactive user interface allowing stakeholders to leverage model predictions, visualize churn trends, and interpret the key factors driving customer behavior. This study culminates in a predictive tool that provides actionable insights, enabling businesses to transition from a reactive to a proactive approach in managing customer relationships. By identifying at-risk customers, companies can deploy targeted interventions to enhance loyalty, reduce churn, and secure long-term profitability, creating a practical bridge between advanced analytics and strategic decision-making
Keywords
Customer Churn, Predictive Modeling, Machine Learning, Customer Retention, Classification, Ensemble Learning, Feature Engineering, Data Science.