Analyzing and Predicting Population Migration to Texas Using Data Science

Over the past decade, Texas has emerged as one of the fastest-growing states in the United States, driven by its abundant economic opportunities, affordable housing, and favorable climate. This population surge significantly impacted urban planning, infrastructural development, and policymaking. The influx of new residents necessitated comprehensive urban planning to meet growing housing, transportation, and public services demands. Additionally, it challenged policymakers to develop strategies for a diverse and expanding population while ensuring sustainable development. Examining migration patterns and identifying primary drivers was essential to effectively anticipate future population growth and address associated challenges. This study employed advanced data science methodologies to analyze population movements in Texas. By integrating socioeconomic factors such as employment rates, housing affordability, and educational attainment levels, the analysis aimed to provide a holistic understanding of migration trends. The primary objective was to build predictive models for forecasting migration patterns into Texas. These models will incorporate diverse socioeconomic variables to improve prediction accuracy and reliability. While the insights from these models could inform strategic decision-making, the project specifically focused on developing and validating the predictive models. The study aimed to provide valuable tools for understanding future migration trends, significantly contributing to migration studies and supporting informed regional planning and development decision-making.