BIG MART SALES PREDICTION SYSTEM

Computer Science and Engineering

Sales forecasting is a crucial task for any retail organization, and Big Mart, as a prominent retail chain, faces the challenge of accurately predicting its future sales to optimize inventory management, staffing, and overall business operations. This abstract provides an overview of a predictive analysis conducted to forecast sales for Big Mart using historical sales data, external factors, and advanced machine learning techniques. The study begins by collecting and preprocessing historical sales data, which includes information on product attributes, store locations, promotions, and sales volume. Additionally, external factors such as economic indicators, weather data, and holiday schedules are integrated into the dataset to account for their potential impact on sales.Several machine learning algorithms, including regression models, time series analysis, and neural networks, are employed to build predictive models. These models are trained and validated using a portion of the historical data, and their performance is assessed based on various evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).The results of the predictive analysis reveal promising outcomes, indicating that accurate sales forecasting can be achieved using these models. The incorporation of external factors significantly improves the accuracy of predictions, allowing Big Mart to adapt its strategies based on changing economic conditions and customer behavior. This study demonstrates the potential for data-driven decision-making in retail, offering Big Mart valuable insights into future sales trends, enabling better stock management, marketing planning, and resource allocation. The implementation of these predictive models can help Big Mart optimize its operations, enhance customer satisfaction, and ultimately increase its competitiveness in the retail industry. Future research can explore real-time data integration and further improve the accuracy and robustness of the predictive models for even more precise sales forecasts.

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