Analysis of Selling Price Determination With Gradient Boosting Algorithm in Traditional Market Stores
DOI:
https://doi.org/10.70609/g-tech.v9i3.7264Keywords:
Gradient Boosting, XGBoost, Selling Price, SHAP, Feature ImportaceAbstract
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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