Modeling online customer purchase intention behavior applying different feature engineering and classification techniques

Abstract In the evolution of digital technology, e-commerce sectors are gradually changing to realize customers’ demands and supply required things with low cost and due time. Recently, various machine learning techniques have been used to investigate different activities of customers and estimate different characteristics and requirements of customers. The goal of this work is to propose a machine-learning model that employs multiple data analytics and machine learning techniques to manipulate customer records and predict their buying intention more precisely. In this study, we collected an online shoppers’ purchasing intention dataset from a public data repository. Different feature transformation methods were employed in the primary dataset and generated its transformed datasets. Besides, we balanced the transformed datasets and detected outliers from…