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Nowadays, competition among business corporations is very high and supermarkets are one such type of business corporation. The most important factor for supermarkets to stay in the competition is their customers’ satisfaction. Providing the customers with the variety of products they need and also the required quantity
under one roof is a big task and to achieve that corporations like Target Corporation, which has several stores across the globe keep track of every product’s sales data. This data store contains the attributes of various items and also the individual customer’s data which is then used to predict potential consumer demand and fulfill
their consumer’s needs as required. Anomalies and general trends are often discovered by mining the data warehouse’s data store. For retailers like Target, the resulting data can be used to predict future sales volume using various machine learning techniques. Therefore, a predictive model was developed using Xgboost, Linear regression, and Ridge regression techniques for forecasting the sales of a business such as Target Corporation, and it was discovered that the model outperforms existing models. It was able to give a brief view of the number of items sold per product.

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