Customer Churn Prediction: Leveraging Data Analysis and Machine Learning Approaches

The prediction of customer attrition in the banking industry has gained significant attention due to the proliferation of services and the need to retain customers in today's competitive market, particularly when customers abandon banking products and services. Customer churn, defined as the termination of a customer's relationship with a bank, has become a critical concern. Changing the banks' approach is crucial, emphasizing the importance of retaining and ensuring customer loyalty, especially when customers discontinue their usage of banking services and products. Banks must actively determine customer attrition by analyzing customer behavior and making efforts and investments to retain customers. This study uses different machine learning algorithms and leverages data from the banking sector to achieve precision in predicting customer loss. This endeavor leads to the empowerment of financial institutions, enabling the implementation of precision-targeted measures aimed at cultivating the preservation of customer loyalty. Before applying machine learning algorithms, data is processed for data cleansing and transformation to ensure data quality. The results indicate that LightGBM and RandomForest algorithms exhibit the best performance in detecting customer attrition.