Predicting Consumer Purchasing Behavior Using Machine Learning Methods

This article explores the application of machine learning methods for predicting consumer purchasing behavior based on data analysis. Using a dataset from Kaggle, data was analyzed and prepared, including removal of duplicates, feature scaling, and correlation analysis. Various machine learning models, such as Random Forest and Gradient Boosting, were tested using different techniques, including hyperparameter optimization and class balancing. The results showed that incorporating feature correlation and hyperparameter optimization significantly improves the accuracy of the models, making them effective tools for predicting consumer behavior in online sales.