Abstract
The purpose of this study is to propose an efficient machine learning model based on five years of data for Seoul Grand Park in Republic of Korea, depending on the weather and day characteristics, and to increase its effectiveness as a strategic foundation for national theme park management and marketing. To this end, the AdaBoost model, which reflects the characteristics of the weather and the day of the week, was recently compared with the actual number of visitors and the predicted number of visitors to analyze the accuracy. The analysis showed 30 days of abnormal cases, and the overall annual distribution was found to show similar patterns. Abnormal cases required details of wind speed, average relative humidity, and fine dust concentration for weather information, and it was derived that more accurate predictions would be possible considering variables such as group visitors, new events, and unofficial holidays.
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