In today’s industry, shopping outlets and Big Mart have adopted an practice of tracking sales data for every product. This helps them predict customer demand and effectively manage their inventory. This paper explores the case of Big Mart focusing on predicting sales, for types of items and understanding the factors that influence these sales. By analyzing features from a dataset collected for Big Mart and employing modeling techniques such as Xgboost, Linear Regression, Gradient Boosting, AdaBoost and Random Forest accurate results are obtained. These findings can then be utilized to make decisions aimed at improving sales performance.

Keywords : Big Mart, XGBoost, AdaBoost, Linear Regression.