Abstract
This paper investigates the comparative performance of some linear predictive models in the presence of multicollinearity. By examining the efficacy of Ordinary Least Squares (OLS), Ridge Regression, Lasso Regression, and Elastic Net Regression, this study aimed to figure out the best method for building robust and interpretable models under such conditions. The research explores how these models address multicollinearity, focusing on coefficient stability, prediction accuracy, and variable selection. Through a rigorous analysis of simulated and real-world datasets, the study shows the strengths and weaknesses of each model, providing valuable insights for researchers and practitioners looking to mitigate the challenges posed by multicollinearity in selecting the most proper method for regression modeling. This will lead to the creation of a model with increased interpretability of the relationships between variables, less variance, and more dependable coefficient estimations.
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