Churn Prediction Based on Customer Segmentation in Banking Industry using Machine Learning Techniques

Given the substantial impact of customer turnover on a bank's financial health, addressing this concern is pivotal for the banking sector. Our project is centered on the development of a machine learning- driven customer churn prediction model to enable banksto proactively identify clients at risk of leaving. Leveraging a comprehensive dataset encompassing transaction history, demographic information, and customer service interactions, we conduct exploratorydata analysis and preprocess the data to unveil behavioral patterns and trends. Employing variousmachine learning techniques such as logistic regression, decision trees, random forests, and support vectormachines, we construct a robust churn prediction model. Model evaluation encompasses multiple metrics, including accuracy, precision, recall, and F1-score. To discern the primary contributors to client attrition, we perform a thorough feature importance analysis. Our results showcase the model's outstanding precision and accuracy in forecasting client attrition, revealing transaction history, customer service interactions, and demographic characteristics as the most influential determinants of churn in the banking sector. In conclusion, our project underscores the efficacy of machine learning approaches in aiding banks to diminish customer churn, enhance retention strategies, and ultimately bolster their financial profitability.