Hybrid AI-Based Sales Forecasting Framework

Abstract

This research explores the utility of advanced artificial intelligence strategies to enhance sales data analysis in the hard financial context of Palestine. Given the area’s chronic political instability, resources, and infrastructure constraints, the research demonstrates how AI-driven models can provide actionable insights to help sustainable commercial enterprise decision-making. The proposed technique integrates three middle algorithms: Linear Regression, Random Forest Regression, and K-Means Clustering. These are mixed into a unique Hybrid Model that leverages supervised and unsupervised learning strategies. Empirical effects showed that whilst Linear Regression carried out properly in shooting linear patterns (R² = 0.819), and Random Forest provided slight effectiveness in modeling non-linear relationships (R² = 0.727), the Hybrid Model outperformed each. By using K-Means to segment the dataset and undertaking localized regression within each cluster, the Hybrid Model achieved superior overall performance, with an R² of 0.895 and an RMSE of 260.15. This approach achieves stronger prediction accuracy and advanced version interpretability via accounting for data heterogeneity. The findings spotlight the sensible value of hybrid AI strategies in complex records environments. Beyond income prediction, such fashions may be extended to domains like finance, healthcare, and client segmentation. For small and medium organizations in resource-restrained areas, adopting hybrid AI strategies gives a path towards extra operational resilience and strategic agility.