Sales Forecasting for Retail Stores

Authors:

Mr. Surendra Bandi¹, G.Akshaya², K.Tharuni³, K.Alekhya⁴, K.Rajini⁵, K.Srija⁶, K.Varun⁷

1Assistant Professor , Department of CSE, Hyderabad, India

2Student of Computer Science and Engineering, HITAM, Hyderabad, India

Abstract: Accurate sales forecasting is vital for retail businesses to optimize inventory management, minimize stockouts and overstock situations, enhance customer satisfaction, and maximize profitability. Traditional statistical forecasting models often struggle to capture the complex, nonlinear patterns inherent in modern retail data, which are shaped by seasonality, promotional activities, holidays, and customer behavior. This study proposes a machine learning–based framework for retail sales forecasting using Light Gradient Boosting Machine (LightGBM), a powerful ensemble algorithm designed for efficiency and scalability. The model leverages historical sales data along with external variables such as pricing strategies, holidays, and store-specific attributes to generate precise demand predictions. Experimental evaluation demonstrates that LightGBM significantly reduces forecasting error compared to classical approaches, offering superior accuracy, robustness, and adaptability. The results highlight the potential of LightGBM as a practical solution for data-driven retail forecasting, enabling better decision-making in demand planning, inventory control, and overall business optimization.

Keywords— Sales forecasting, retail analytics, LightGBM, machine learning, demand prediction, inventory management, data-driven decision making.