Comparative Analysis of Machine Learning Algorithms for Pharmacy Sales Prediction, Demand Forecasting, and Medicine Sales Category Classification

Physical techniques combined with Machine Learning now predict pharmacy sales data alongside forecasted demand trends to optimize distribution networks and inventory processes for better performance results. Demand Forecasting drives the best possible decisionmaking regarding drug supply chain operation and medication stock management resulting in increased overall performance. Inventory for performance enhancement. Python-based machine learning algorithms Their program uses Machine Learning methods to generate drug sales trend predictions enabling forecasting of upcoming market demands. demand patterns. The system uses historical sales points data and seasonal transformation techniques Market pricing evaluation alongside analysis determines the system's optimal inventory decisions. amounts and optimal pricing methodologies. The plan achieves resource effectiveness The system prevents drug shortages by both stopping stockouts together with optimizing inventory sizes. The system optimizes inventory sizes both to minimize operational expenses and to eliminate drug shortages. The incorporation of Supply relations benefit through the adoption of automated prediction algorithms powered by machine learning systems. Reliable restocking processes work together with improved order control and delivery performance through the system. delivery performance. Web functionality as part of the system provides data-driven connectivity between system components. Users can use analytics tools to run forecast examinations while building trend forecasts that guide organizational decisions. The predictive analytics together with automated solution serve customers. The system creates efficient supplydemand connections to support pharmacies through systems that develop optimal market responses and service delivery while enhancing healthcare support for patients.