Optimizing Linear Regression Models with Lasso and Ridge Regression: A Study on UAE Financial Behavior during COVID-19

Abstract

This study examines the way individuals in the United Arab Emirates handled their finances during the COVID-19 pandemic, with a focus on optimizing linear regression models utilizing Lasso and Ridge Regression methods. Using the World Bank's enormous Global Findex dataset, we navigated the obstacles of multicollinearity and missing values. Our investigation showed that specific economic indicators and demographic characteristics, including gender and educational attainment, are crucial in affecting financial choices during crises. Our study carefully analyzed the performance of Lasso and Ridge Regression in the context of the Global Findex dataset. Ridge Regression was outperformed by Lasso, which is renowned for its feature selection and complexity reduction abilities. This research provides a comprehensive knowledge of the way advanced regression approaches might improve predictive modeling in such settings, shedding light on the complex dynamics of financial activity during a worldwide crisis.