Management System with Sales Forecasting and Inventory Optimization

Retail shop owners often struggle to make informed business decisions because sales data are managed and analyzed manually, which demands considerable time and effort. Historical sales analysis is essential because it enables future sales prediction and informs improved decision-making. To solve these problems, an AI-Driven Smart Retail Shop Management and Sales Forecasting System has been developed using a machine learning algorithms. The system was built with Python and libraries including Pandas, NumPy, and Scikitlearn. Streamlit provides the application interface, whereas Power BI creates interactive dashboards and charts from the selected dataset. The system applies three machine learning algorithms: Linear Regression to forecast sales, K-Means Clustering to segment customers, and the Apriori algorithm to recommend products according to product associations. It also offers sales analysis, alert notifications, inventory management, and product recommendation features. Overall, the system is designed to enable retail shop owners to interpret sales data, identify business trends, and make more informed business decisions