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An Effective Predicting E-Commerce Sales & Management System Based on Machine Learning Methods

Journal of Artificial Intelligence and Big Data | Vol 1, Issue 1

Table 1. Summary of literature review one-commerce-based sales prediction using machine learning

AuthorMethodologyDataFindingsLimitations
Niu (2020)XGBoost with feature engineering for sales predictionWalmart sales dataset from KaggleXGBoost outperformed Logistic Regression and Ridge Regression; RMSSE was 0.141 and 0.113 lower than these models, respectively. Feature importance was ranked.Limited comparison to other models; only Walmart sales data used.
Wisesa, Adriansyah & Khalaf (2020)Gradient Boosting for B2B Sales PredictionB2B sales dataGradient Boosting had good accuracy with MSE = 24,743,000,000 and MAPE = 0.18. Generated reliable, accurate sales forecast data.Lack of detail on specific data attributes; limited evaluation beyond MSE and MAPE.
Ding et al. (2020)CatBoost with feature engineering for sales predictionWalmart sales datasetCatBoost outperformed traditional models such as SVM and linear regression with an RMSE of 0.605. Required less fine-tuning and improved generalisation.Only Walmart sales data used; limited comparison to other ensemble models.
Kulshrestha & Saini (2020)Sales prediction based on ML, split data by quarters and predicted future income.E-commerce sales dataPredicted future income and analyzed frequently sold commodities. Provided strategic insights for inventory and business planning.Did not specify the exact ML algorithm used; limited comparison to other models.
Kaneko & Yada (2016)Deep learning model with L1 regularization for sales prediction3 years of POS data from a retail storeAchieved 86% accuracy for sales prediction. Deep learning model outperformed Logistic Regression, with accuracy falling by 7% versus 13% for Logistic Regression.No mention of computational complexity or scalability for larger datasets. Only tested on retail POS data.
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