This study explores the prediction of optimal store types for new openings along Tokyo's Yamanote Line by analyzing station passenger flow data. Leveraging open databases and machine learning techniques, we develop a predictive model designed to support businesses in identifying suitable store types based on local conditions. Using public data from the Japanese Statistics Bureau, e-Stat, and Seikatsu Guide.com, we gather comprehensive statistics, including station information, census data, station traffic volumes, consumer expenditure, and surrounding geographic data. These datasets, especially within a one-kilometer radius of each station, provide a foundation for constructing a robust database to inform predictions. Our machine learning approach applies Random Forest to determine key influencing factors across industries, followed by evaluations using k-Nearest Neighbor, Decision Tree, Random Forest, and XGBoost classifiers. Results indicate that the Random Forest model outperforms other classifiers, achieving the highest F1 score of 0.4321, thereby validating its effectiveness in our interactive recommendation system, which offers predictive insights to guide store placement decisions.