Predictive Modeling Techniques for Complex Multivariate Data
TL;DR
Comparative analysis of Multiple Linear Regression, Random Forest, Support Vector Regression, and Artificial Neural Networks demonstrates that ensemble and neural network-based models outperform traditional methods, particularly in handling noisy and highly correlated variables.
Abstract
Predictive modeling has become increasingly important as digital transformation, sensing technologies, and large-scale data collection generate complex multivariate datasets across domains such as healthcare, finance, engineering, and industry. These datasets often contain nonlinear relationships, uncertainty, noise, and high dimensionality, making traditional statistical methods less effective. Advanced machine learning techniques, including Random Forests, Support Vector Regression, Artificial Neural Networks, and deep learning models, offer improved predictive accuracy and robustness for such complex data. This study presents a systematic review of predictive modeling approaches and proposes a framework comprising data preprocessing, feature selection, model development, training, validation, and performance evaluation. Comparative analysis of Multiple Linear Regression, Random Forest, Support Vector Regression, and Artificial Neural Networks demonstrates that ensemble and neural network-based models outperform traditional methods, particularly in handling noisy and highly correlated variables. The findings also highlight the importance of feature selection and dimensionality reduction in improving computational efficiency and model generalization. The proposed framework provides valuable guidance for developing reliable predictive systems in modern data-driven environments.