Smart Retail: Utilizing Machine Learning for Demand Prediction, Price Strategy, and Inventory Management

This research demonstrates the ability of Artificial Intelligence (AI) to enhance real-time demand forecasting, price prediction, and inventory management by analyzing key factors such as competitor prices, customer preferences, and stock levels. The Random Forest Regressor (RFR) model has been implemented to achieve a Mean Squared Error (MSE) of 8.15% for demand forecasting and 1.11% for price prediction, highlighting its accuracy. These AI-driven insights allow businesses to make smarter, faster decisions, optimize pricing strategies, and adjust inventory levels to meet market demands. Additionally, the model helps avoid surplus or deficit, ensuring seamless inventory flow, reducing operational costs, and improving overall profitability. AI integration into retail processes allows companies to respond dynamically to real-time changes and maintain a competitive edge in a fast-paced market environment.