Building and Comparing Machine Learning Models to Predict Repeat Customers at Ramen Restaurants in the Kanto Region

Abstract

In the food service industry, promoting repeat visits from existing customers is regarded as a cost-effective strategy, and accurately predicting such behavior is considered valuable for improving services and promotional efforts. However, repeat behavior depends on a wide range of factors, making it difficult to achieve high prediction accuracy using conventional heuristic approaches. Although machine learning techniques have been increasingly applied in recent years, investigations into models with higher generalization performance remain insufficient. Therefore, this study constructs multiple machine learning models to predict customers’ intention to revisit ramen restaurants in the Kanto region, using input features such as service experience and location conditions, and conducts a comparative evaluation of the models’ predictive performance. The findings are expected to contribute to the establishment of practical prediction methods for repeat customers in the food service sector.