Leveraging A Random Forest Web Application For Improved E-commerce Sales Forecasting

Abstract

Accurate Sales Forecasting Is Critical For E- Commerce Businesses To Optimize Inventory Management, Reduce Costs, And Improve Customer Satisfaction. This Paper Introduces A Web Application That Leverages The Random Forest Algorithm To Enhance E-commerce Sales Predictions. The Application Features A User-friendly Interface And Automated Workflows For Data Upload, Model Training, Prediction Generation, And Visualization. Methodology, Implementation, And Performance Improvements Achieved Using Random Forest Over Traditional Forecasting Techniques Are Discussed. By Automating Complex Processes, The Application Ensures Accessibility For Non-technical Users And Enables Data-driven Decision-making.

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