Supermarkets generate a lot of information every day. This information includes what products are sold, how many of these products are sold, at what price they're sold and how people purchase them. Many supermarkets do not make use of this information. They mostly use their billing systems to process sales not to gain knowledge from the information. If they properly examined this information they could learn about the shopping habits of their customers how money they are making and how demand for products changes. This study uses data analysis and machine learning to help supermarkets make decisions. First, we prepare the supermarket data. By understanding these things GS supermarkets can make decisions and run more smoothly. We use tools like Linear Regression and Decision Tree algorithms to predict sales at supermarkets. We check how well these tools work by looking at their accuracy and other measures. We found that by examining billing data from supermarkets can make decisions about inventory prediction, forecast demand for products and plan promotions. Supermarket billing data can be very useful. It is about turning this data into something so supermarkets can make decisions and stay ahead of other supermarkets. By using data analysis and machine learning supermarkets can understand what is working for supermarkets and what is not and make changes. This can lead to sales, happier customers and a more sustainable business, for supermarkets.
Supermarket Billing Analysis, Retail Data Analytics, Sales Forecasting, Machine Learning, Inventory Optimization, Customer Behaviour Analysis
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