This study proposes a data-driven model for optimizing the location of Chinese restaurants, a critical determinant of success in the highly competitive food service industry. Using public data provided by the Korean government—including resident registration statistics and restaurant business records—the analysis quantifies the saturation of Chinese restaurants across administrative districts in South Korea. Two indices are defined: the Saturation Index (number of Chinese restaurants per 10,000 residents) and the Competitiveness Index (proportion of Chinese restaurants among all food service businesses). Data preprocessing and analysis were conducted in Python, with visualization facilitated by Pandas, Matplotlib, and Seaborn. The results reveal regional disparities: for example, Gwacheon and Sejong exhibited low saturation levels, indicating substantial market potential, while districts such as Gangnam in Seoul and Haeundae in Busan were oversaturated. The findings demonstrate the practical utility of public data in entrepreneurial decision-making and present a reproducible analytical framework that can be extended to other restaurant types or industries. This approach advances location selection from intuition-driven to evidence-based, quantitative analysis.