| dc.description.abstract | The supermarket industry operates in a highly dynamic and competitive environment, where accurate sales forecasting plays a crucial role in inventory management, resource allocation, and strategic decision-making. This abstract presents a Supermarket Sales Forecasting System designed to provide reliable and accurate sales predictions for supermarkets. The system utilizes advanced data analytics techniques and linear regression machine learning algorithm to analyze historical sales data, identify patterns and generate forecasts. By considering factors such as seasonality, promotional activities and external influences, the system aims to capture the complex dynamics of the supermarket industry. The super sales system incorporates data preprocessing to handle data cleansing and normalization. The system is able to forecast weekly and monthly sales. Usability and accessibility are key aspects of the Super Sales System. The system provides an intuitive user interface that allows supermarket managers and decision makers to interact with the forecasting results and make informed decisions. To validate the effectiveness and accuracy of the system, extensive testing and evaluation are conducted. The evaluation includes measures such asforecast accuracy and error analysis. The results demonstrate the superiority of the Supermarket Sales Forecasting System in terms of accuracy, reliability and usability. The implementation of this system offers significant benefits to the supermarket including improved inventory management, optimized resource allocation, enhanced decision making processes and increased profitability. By providing timely and accurate sales forecasts, the system empowers supermarkets to streamline operations, reduce costs and meetcustomer demands more effectively. | en_US |
