Predicting Customer Gender from Online Shopping Behavior Patterns

This study offers a machine learning method for determining a customer's gender based on their internet purchasing habits. To create a prediction model, the study makes use of data elements like browsing history, purchasing trends, and product categories. XGBoost, Random Forest, and Support Vector Machines (SVM) are some of the algorithms used to categorize customers according to their purchase habits. In an effort to improve the accuracy and performance of the model, feature selection approaches are used. The suggested model shows good accuracy in identifying the gender of the customer, offering insightful information for focused marketing campaigns and customized shopping experiences. The results demonstrate how machine learning may be used to better e-commerce platforms by analyzing consumer behavior.