SALES PREDICTION AND ANALYSIS OF SUPERMARKETS USING RIDGE AND POLYNOMIAL REGRESSION TECHNIQUES
Abstract
Due to the obvious rapid growth of global shops including e - shopping, daily competition amongst numerous shopping complexes as well as large super markets is growing fiercer& more aggressive. Every marketplace tries to attract the interest of consumers by providing tailored as well as restricted discounts, such that the quantity of revenues with each product can be projected for the company's managing inventory, shipping, including logistical services. Nowadays, supermarkets run own branches and franchises, known as Big Marts, keep records of every product's revenue information in order to forecast possible customers' needs & adjust inventory control. Monitoring the information warehouse's storage space is a common way to find abnormalities as well as general patterns. This generated data may be utilised by merchants like Big Mart to anticipate the future sales volume using different machine learning approaches. For estimating the sales of the company such as Sales -Mart, a predicting model is constructed utilizing XGBoost, Linear regression, Polynomial regression, as well as Ridge regression approaches, and that it reveals that the system provides the best model.
Keyword : Big Marts; Anomalies; sustainable; machine learning
This work is licensed under a Creative Commons Attribution 4.0 International License.
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