An Online Shoppers Purchasing Intention Model Based on Ensemble Learning

The sales trends and the visitors' purchase intention of e-commerce are crucial issues for companies. Therefore, companies want to know whether products are purchased based on customer visits. This study proposes an ensemble learning-based model to analyze customers' purchase intentions for e-commerce companies. It is possible to create a more accurate model using several machine learning methods within a system, which is the ensemble learning approach. The prediction performance of the proposed model is evaluated by different metrics, such as accuracy, precision, recall, AUC, and F-score. This paper aims to improve the purchasing rates of online shopping sites by determining the rate of visitors who intend to purchase but leave the site without taking action by using information such as the click flow of visitors.