<![CDATA[Traditional market traders often face difficulties in determining optimal selling prices amid competition from modern retailers. This study aims to apply and compare Gradient Boosting and XGBoost algorithms to develop a selling price prediction model for traditional market stores. The research utilizes two datasets : a large-scale dataset from annual sales data and a small-scale dataset from one month of sales. Model training involves hyperparameter optimization using GridSearchCV and evaluation through metrics such as RMSE, MAE, R², and MAPE. Additionally, feature importance and SHAP analyses were conducted to interpret model behavior. The results demonstrate that both models performed well, with R² values nearing 1.0 and MAPE below 2%. Gradient Boosting outperformed XGBoost on the large dataset, while XGBoost showed better accuracy on the small dataset. These findings highlight the potential of machine learning in supporting data-driven pricing strategies for traditional markets.]]>
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