Abstract
The purpose of this study is to investigates the gender differences of Shopee platform for online shopping behavior by using the J48 decision tree algorithm to classify and predict shopping frequency among male and female consumers for Malaysia context. WEKA software was used in this study to analyze the datasets. From the experiments, the majority of Shopee user were female consumers. The findings shows that female consumer behavior is more complicated and more varied regarding purchasing behavior. The study's findings demonstrate the potential of gender specific insights to enhance e-commerce strategies, particularly in product recommendations and targeted marketing. Although the J48 model performed well in predicting male shopping patterns, it was less effective for females, indicating the need for more advanced modeling techniques is used to better capture the complexities of female consumer behavior. This research also emphasizes the significance of using machine learning tools like the J48 decision tree to analyze consumer data, providing valuable insights for improving customer satisfaction and business performance. However, limitations such as sample size and the focus on a single platform suggest that further research is needed, including the exploration of alternative algorithms and broader demographic factors.
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