<![CDATA[This study is motivated by the limited ability of handicraft Micro, Small, and Medium Enterprises (MSMEs) to analyze the key factors influencing sales success on e-commerce platforms, despite the availability of historical transaction data. Previous studies generally applied classification algorithms without systematic hyperparameter optimization, potentially leading to suboptimal models and overfitting issues. To address this gap, this research proposes the implementation of a Decision Tree algorithm optimized using Grid search Cross-validation. The dataset was obtained from the Brazilian e-commerce platform (Olist Dataset), specifically the ‘artes’ category as a proxy for handicraft products, with an 80:20 split for training and testing data. The optimization process explored 576 parameter combinations to determine the best configuration. The optimized model achieved an accuracy of 97.61% with a simplified tree structure (max_depth=None), enhancing interpretability. Feature importance analysis product_height_cm as the most dominant factor (64.23%), followed by product_height_cm, product_width_cm, Freight_value, product_weight_g, and price. These findings demonstrate that the combination of Decision Tree and Grid search effectively produces an accurate and interpretable predictive model, providing strategic decision-making support for handicraft MSMEs in digital marketplaces.]]>
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