A Machine Learning Classification to Modeling Undocumented Migration from Mexico to the United States

Machine learning (ML) techniques are broadly employed in several domains, including economics, business, and medicine, to support decision-making. Many works have used ML to gain insights into human migration and its origins and specific characteristics. In this work, we present an application of several supervised ML classification algorithms as an effort to predict the likelihood of Mexican immigrants remaining in the United States following their latest undocumented migration trip based on historical data that includes personal-level social, employment, financial, and migratory experience information retrieved by the Mexican Migration Project (MMP) over 37 years.