Artificial intelligence (AI) and machine learning (ML) help in making predictions and businesses to make key decisions that are beneficial for them. In the case of the online shopping business, it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business. In this research, a dataset of 12,330 records of customers has been analyzed who visited an online shopping website over a period of one year. The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future. The permutation feature importance approach has been used to get the importance of features according to the output variable (Revenue). Five ML models i.e., decision tree (DT), random forest (RF), extra tree (ET) classifier, Neural networks (NN), and Logistic regression (LR) have been used to make predictions on the unseen data in the future. The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score, precision, recall, F1 score, and ROC-AUC curve. RF model is the best model among all five chosen based on accuracy score of 90% and F1 score of 79% followed by extra tree classifier. Hence, our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour. Our research also reveals the importance of page value as a key feature for capturing online purchasing trends. This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

Explainable artificial intelligence; online retail; neural network; random forest regression