This project develops StockMinds, a responsive web-based inventory management system that optimizes stock levels through real-time monitoring and machine learning. The system integrates predictive features for low-stock forecasting, helping businesses reduce the risk of overstocking or stockouts. By utilizing historical sales data, StockMinds automatically suggests reorder levels based on demand patterns and predicts stock needs, allowing for automated reordering. Key features of the system include product CRUD operations, stock tracking, low-stock alerts, and advanced reporting capabilities. The platform ensures seamless functionality across various screen sizes, providing a user-friendly interface for both admin and staff users.

Keywords: Inventory Management, Stock Prediction, Reorder Suggestions, Product CRUD, Reporting, Low-Stock Alerts