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Majority of the revenue from the banking sector is usually generated from long term deposits by customers. It is for banks to understand customer characteristics to increase product sales. To aid this, marketing strategies are employed to target potential customers and let them interact with the banks directly, generating a large amount of data on customer characteristics and demographics. In recent years, it has been discovered that various data analysis, feature selection and machine learning techniques can be employed to analyze customer characteristics as well as variables that can impact customer decision significantly. These methods can be used to identify consumers in different categories to predict whether a customer would subscribe to a long-term deposit, allowing the marketing strategy to be more successful. In this study, we have taken a R programming approach to analyze financial transaction data to …