Implementation of Machine Learning Techniques for Big Mart Sales Forecast Analysis
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Abstract
The sales data for each individual item is now tracked by supermarket run-centers, Big Marts, in order to forecast possible consumer demand and revise inventory control. By mining the data store of the data warehouse, wide-ranging trends as well as anomalies are frequently found. The generated data may be utilised by merchants such as Big Mart that employ a number of machine learning techniques in order to forecast forthcoming sales volume. A prediction algorithm was developed to estimate the sales of an organisation like Big-Mart utilising Xgboost, Linear regression, Polynomial regression, also Ridge regression methodologies. It was shown that the model beats other models.
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