Enhancing sales volume using machine learning algorithms

Abstract

In today's highly competitive business landscape, companies face a significant challenge in making accurate decisions based on vast amounts of historical data. Reliance on human data analysis often leads to biases and errors, hindering the ability to extract effective insights for sales forecasting. To address this challenge, this research presents an advanced model that integrates 14 machine learning (ML) regression algorithms, including XGBRegressor and LGBMRegressor, to provide accurate sales predictions using a comprehensive global store dataset. The results demonstrate that XGBRegressor and LGBMRegressor achieved the highest test accuracy (92%) and the lowest error rates, proving their ability to handle complex prediction tasks efficiently. This high accuracy in sales forecasting enables companies to make more effective strategic decisions, such as optimizing inventory management, allocating resources optimally, and exploring new growth opportunities. Consequently, the use of these advanced algorithms directly contributes to increasing sales volume and achieving a sustainable competitive advantage.