Churn Prediction in Banking Sector

The membership of a banking system depends on the interest provided for the account , charges on the transactions and the services provided on the account. Now-a-days, many banks provide competitive services which give customers options to choose from. This might lead to customer dissatisfaction and result in the deactivation of the membership also known as churn. Churn is ‘when a client cancels a membership to a company they have been using. In this project we will try to predict the churn from the previous databases of the banking systems and study it using data visualization. In current days, the customers are getting more attracted towards the quality of service (QoS) provided by the company. As a result, customer churn and engagement has become one of the main problems for most of the banks. In this project, we have used a voting approach of SVM and Random Forest to find the most successful and accurate way to predict customer churning or retention.