Project overview
Retail businesses rely on accurate sales forecasting to optimize inventory, pricing, and supply chain decisions. This project focuses on predicting Walmart’s sales trends using historical data and machine learning models to improve demand planning and operational efficiency.
By leveraging time-series forecasting techniques such as Linear Regression, ARIMA, and XGBoost, we analyzed key factors affecting sales, including seasonality, promotions, and economic trends. The goal was to reduce stockouts, minimize overstocking, and optimize supply chain management, ultimately driving better business performance.
This project demonstrates how AI-driven predictive modeling can provide valuable insights for large-scale retailers, enabling data-driven decision-making for improved efficiency and profitability.
