Ensemble Learning for Enhanced Prediction of Online Shoppers’ Intention on Oversampling-Based Reconstructed Data

As customer traffic has been increasing over the years on online shopping websites, it is indispensable for sellers to assess online customers’ purchase intentions, which can potentially be predicted by analyzing the historical activities of the customers. This study analyzes the highly imbalanced empirical data of online shoppers’ intentions to foretell whether a visitor to an online shopping website will make a purchase. The synthetic minority oversampling technique has been implemented to reconstruct the dataset to alleviate the class imbalance in the original dataset. The effectiveness of oversampling has been identified by comparing the predictive performance of four different classifiers Partial decision tree (PART), decision tree (DT), Naïve Bayes (NB), and logistic regression (LR) on the reconstructed data with the performance on the original dataset. It has been observed that each classifier performs better on the reconstructed dataset. Ensemble learners have been implemented with varying base classifiers on the reconstructed dataset to identify the best predictive model. Bagging, boosting, and max-voting ensemble learners have been implemented with the base classifiers PART, DT, NB, and LR. The best performance has been observed by the prediction using bagging with PART as the base classifier with an accuracy of 92.62%. Hence, it has been identified as the best model for predicting the purchase intention of a customer in terms of accuracy. However, the highest precision and recall values of 0.923 have been given by the max-voting classifier with DT, PART, and LR as the base learners. It has also been concluded that the proposed methodology outperforms the existing models for shoppers’ intention section tasks.