A Hybrid Metaheuristic Model for Efficient Analytical Business Prediction

Accurate and efficient business analytical predictions are essential for decision making in today's competitive landscape. Involves using data analysis, statistical methods, predictive modeling to extract insights make decisions. Current trends focus on applying analytics predictions. Optimizing involves increasing the accuracy efficiency of models used forecast future trends, behavior, outcomes environment. By analyzing developing optimization strategies, businesses can improve their operations, reduce costs, increase profits. The analytic method uses a hybrid PSO (Particle Swarm Optimization) GSO (Gravitational Search algorithm effectiveness decision-making process business. In this approach, is explore search space find global best solution, while refine around solution. meta-heuristic optimizes three components analytics: descriptive, predictive, perspective. model designed strike balance between exploration exploitation, ensuring effective convergence high-quality solutions. results show that R2 value each parameter close one, indicating more fit model. RMSE measures average prediction error, with lower error performing well. MSE represents mean squared difference predicted optimized values. A indicates higher level accuracy.