<![CDATA[The increasing number of visitors to e-commerce websites is not always accompanied by a corresponding increase in purchase transactions, making it difficult for companies to identify visitors with high conversion potential. In addition, using all available attributes may increase model complexity without necessarily improving predictive performance. This study analyzes the impact of Chi-Square Feature Selection on the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting website visitor conversion. The study uses the Online Shoppers Purchasing Intention dataset consisting of 12,330 instances with 17 predictor attributes and one target attribute. The research process includes exploratory data analysis, preprocessing, Chi-Square-based feature selection, classification model development using Random Forest and XGBoost, and evaluation using Accuracy, Precision, Recall, F1-Score, Matthews Correlation Coefficient (MCC), and Area Under the Curve (AUC-ROC). Four experimental scenarios were evaluated: all features (baseline), Top-15, Top-10, and Top-5 selected features. The results show that the baseline model using all features achieved the best overall performance. The Random Forest baseline model obtained an Accuracy of 90.05%, Precision of 73.94%, F1-Score of 63.13%, and MCC of 0.5835, while the XGBoost baseline model achieved the highest AUC-ROC of 0.9271. Furthermore, PageValues, BounceRates, ExitRates, ProductRelated_Duration, and ProductRelated were identified as the most influential features affecting visitor conversion. The main contribution of this study is providing empirical evidence that Chi-Square Feature Selection is more effective in reducing feature complexity and identifying relevant attributes than improving classification performance on the Online Shoppers Purchasing Intention dataset, offering practical guidance for feature selection strategies in machine learning-based website conversion prediction]]>
