Prediction of Buying Intention: Factors Affecting Online Shopping

Recent times have witnessed a very significant increase in the use of electronic commerce all over the world. The fact that sales have been steadily climbing demonstrates that there is massive untapped market potential for online shopping. The process of analysing data to determine and categorise the intentions of online shoppers contributes to the accumulation of store earnings. The application of machine learning classification models to the data collected from e-commerce websites is the main goal of our endeavours. Specifically, We investigate whether machine learning is a reliable method for predicting the possibility that a consumer who explores a retailer's website will actually make a purchase. Exploratory data analysis was carried out so that we could visually examine our information and search for any patterns or trends that may have been there. In this article, we present a method that is capable of predicting the purpose of consumers who visit an e-commerce site to make a purchase based on data obtained while these users are browsing the websites in question. The Online Consumer Buying Sentiment Dataset that can be found in the UCI Machine Learning Repository is one that we use.