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- W4313444811 abstract "In this paper, a comparative study is conducted on machine learning models used for predictive analytics of sales. Sales prediction is an important part of modern business intelligence and proves useful toward better stock administration and staff scheduling which in turn boosts deals and consumer loyalty. To prevent over-stocking and under-stocking, every retail store must forecast revenue and inventory requirements in advance. This enables the company to offer the best possible customer service while avoiding losses, guaranteeing the store’s long-term viability. The dataset selected is of the Rossmann chain of drug stores. Feature engineering helps understand the importance of various features and to clean the data by handling outliers and missing data. It is observed that the dataset is historical in nature and has seasonal trends. ARIMA Model, Facebook’s Prophet Model and XGBoost Model have been chosen for comparison. The Root Mean Square Error (RMSE) is employed to quantify the prediction accuracy because of the nature of our situation. The results show us that ARIMA Model fairs well compared to the others as it has the lowest value of 739.06." @default.
- W4313444811 created "2023-01-06" @default.
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- W4313444811 date "2023-01-01" @default.
- W4313444811 modified "2023-09-29" @default.
- W4313444811 title "Sales Prediction Using ARIMA, Facebook’s Prophet and XGBoost Model of Machine Learning" @default.
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- W4313444811 doi "https://doi.org/10.1007/978-981-19-5868-7_9" @default.
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