AN OPTIMIZED MACHINE LEARNING FRAMEWORK FOR ACCURATE PREDICTION IN COMPLEX COMPUTATIONAL SYSTEMS

Abstract

This study developed and evaluated an optimized machine-learning framework for accurate prediction in complex computational systems, addressing the need for predictive models that can simultaneously achieve high accuracy, computational efficiency, robustness, and reliable generalization. A quantitative computational research design was adopted, using a dataset comprising 1,000 observations collected and prepared for machine-learning analysis. Data were collected in a structured computational dataset and were subjected to preprocessing, missing-value treatment, duplicate detection, transformation, and feature optimization before model development. The proposed framework integrated feature selection, machine-learning model development, hyperparameter optimization, cross-validation, prediction, and performance evaluation within a unified computational workflow. Python was used for data analysis, with Pandas and NumPy applied for data management and numerical processing, while Scikit-learn was used for feature selection, model training, hyperparameter optimization, validation, and performance evaluation. The findings showed that the optimized framework improved prediction performance compared with conventional and non-optimized machine-learning approaches and provided more consistent performance across training, validation, and testing conditions. Feature optimization reduced unnecessary variables and contributed to improved computational efficiency, while hyperparameter optimization enhanced model configuration and predictive capability. The framework also demonstrated comparatively strong robustness and generalization when evaluated under varied data conditions. The study is significant because it provides an integrated and systematic approach for developing predictive models that considers accuracy, computational efficiency, robustness, and generalization simultaneously rather than focusing on prediction accuracy alone. The proposed framework can support researchers and practitioners in developing more efficient, reliable, and scalable machine-learning solutions for complex computational prediction tasks.