Online Shoppers' Purchase Intention using Ensemble Learning Approach

Abstract

The customer’s purchase intention can be predicted by analyzing the history of the customers. In this study, we have analyzed the data of online shoppers for building a better prediction model to predict their purchase intention. Initially exploratory data analysis is performed on the dataset. We have used different ensemble algorithms such as Random Forest, Gradient Boosting, XGBoost and LightGBM to predict whether a customer, visiting the website of an online shop, will end up with a purchase or not. Later we have performed ensemble methods to boost up the performance of the algorithms using SMOTE to overcome class imbalance. Lastly model performance evaluation is done using parameter tuning. Study has shown that XGBoost model predicts with 89.97 % accuracy on imbalanced data, whereas Random Forest displays 93% accuracy after using SMOTE to predict the customer’s purchase intention. Moreover, XGBoost shows the highest accuracy, which is 93.54% after parameter tuning. Execution time of the models is also observed in the study.