Bank Marketing Data Classification Using Machine Learning

Abstract: In today's world, where huge amount of data is generated in every field of day to day activities, banking sector is one of them. As an outcome of work, various machine learning concept are studied with respect to Bank marketing data classification. Banking is a provision of the services by bank to an individual customer. The dataset is originally collected from UCI Machine learning repository and Kaggle website. The data is related to bank marketing campaigns of banking institution based on phone call. In this work, Python is used as a coding language and Machine learning concept is used as statistical learning for data analysis. The main reason of using machine learning is to build a predictive model to produce the better prediction. The outcome of the result is analyzed with supervised Naïve Bayes algorithm for classification purpose. The main objective of building the model is to describe whether the customer has opted for term deposit. The bank should target the potential customer with considerable amount of time responding to the phone calls. The work implemented resulted in measuring accuracy, precision, recall and F1 score, towards term deposit prediction.

Keywords: Bank marketing, Customer, Machine learning, Deposit prediction