Comparison of Boosting Algorithms (LightGBM, CatBoost, and XGBoost) on Ship Ticket Sales Prediction
Keywords:
Comparison, Boosting, LightGBM, CatBoost, XGBoostAbstract
Usually, in optimizing ship capacity and knowing the ship's departure schedule in the next period, the ship management makes predictions based on data from the previous period. However, the problem that occurs is the shortage of ticket sales on holidays and a decrease in ticket sales on weekdays, making it difficult for ship management to plan departures and determine schedules. Therefore, a way is needed that can make accurate predictions from previous predictions that only look at previous sales data without using better calculations based on sales data. Accurate predictions can help in planning ship capacity, scheduling, determining ticket prices, as well as marketing strategies. The prediction methods used can vary, from traditional statistical approaches to the use of machine learning and artificial intelligence that utilize historical data on ticket sales as well as appropriate external factors. Therefore, research on the prediction of sea ticket sales is an important step to improve the efficiency and profitability of shipping companies. By understanding sales patterns and trends, companies can be better prepared to face fluctuations in demand and maximize business opportunities in the marine transportation sector. There are many methods in machine learning, including LightGBM, CatBoost, and XGBoost. This study makes a comparison of the three methods so that we can find out which method is closer to the prediction results of sea ticket sales. So it is expected that.
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