Business Forecasting Using Machine Learning Algorithms

Abstract

Business forecasting plays a crucial role in helping organizations predict future trends and make informed decisions. Traditional forecasting methods often struggle with complex and rapidly changing business environments. Machine learning (ML) overcomes these limitations by learning patterns from historical and real-time data, improving prediction accuracy and adaptability. This study reviews ML-based forecasting techniques, covering data preprocessing, feature engineering, model selection, training, and performance evaluation using metrics such as MAE, RMSE, MAPE, and R². It also highlights applications in finance, retail, manufacturing, healthcare, logistics, and e-commerce. The findings indicate that ensemble and deep learning models outperform conventional statistical methods, offering scalable, intelligent, and data-driven forecasting solutions that enhance business performance and strategic decision-making.