<![CDATA[The rapid growth of e-commerce platforms has generated a massive volume of transaction data that can be utilized to understand consumer behavior. However, data complexity and the heterogeneity of customer characteristics present challenges in analysis. This study aims to classify consumer behavior using the Decision Tree algorithm based on available data patterns. The dataset used is the Drug Classification Dataset from Kaggle, which contains both numerical and categorical attributes. The research methodology includes data preprocessing, data splitting, model development, and evaluation using accuracy, precision, and recall metrics. The results indicate that the Decision Tree algorithm achieves high accuracy and produces an interpretable model through decision tree structures and if-then rules. This advantage makes Decision Tree effective in supporting managerial decision-making, particularly in consumer segmentation-based marketing strategies.]]>
Copyrights © 2026
