Inventory Demand Forecasting Using Machine Learning

This exploration presents a robust and interactive soothsaying system for force demand using Random Forest Regressor and Prophet model integrated into a stoner-friendly Streamlit- grounded web interface. The operation accepts force data with product-wise and date-wise deals, calculates net demand after counting for returns, expirations, and damages, and offers demand soothsaying and reduction simulations. crucial factors include an eco indicator for sustainability, rear logistics analysis, and amped demand visualizations. The model allows decision- makers to estimate unborn stock conditions, reduce waste, and optimize force chain effectiveness using real- time data analytics.