Churn Modeling Using Artificial Neural Network

In recent years, banks have observed increased customer departures for various reasons. Churn modeling is essential to formulating effective strategies for customer retention. This study aimed to anticipate whether a customer is likely to leave the bank in the foreseeable future. Churn prediction involves identifying clients likely to discontinue service or terminate their subscription. This prediction holds significant importance for many firms, as acquiring new customers often incurs higher costs than retaining existing ones. The dataset utilized includes diverse parameters such as credit score, age, gender, estimated salary, etc. In addressing this issue, neural networks prove advantageous as they can adeptly learn and model nonlinear and intricate interactions. This capability is vital because many real-life relationships between inputs and outputs exhibit non-linear and complex patterns. Consequently, an Artificial Neural Network with two hidden layers is employed within the Python environment. The study delves into the correlation between various parameters, conducting thorough exploratory data analysis to discern the key factors influencing a customer's decision to discontinue their association with the bank.