Abstract

Accurately predicting store sales is essential for businesses to optimize inventory management, marketing strategies, and staffing. Traditional sales prediction models often rely on historical data and simple linear trends, but these methods can be limited in capturing the complexity of factors that affect sales. This paper explores the application of machine learning (ML) algorithms to predict store sales, considering factors like promotions, holidays, weather conditions, and seasonal trends. We analyze various machine learning models, evaluate their performance, and demonstrate how they can be used to improve sales forecasting and operational efficiency.