Feature Selection Based on Artificial Bee Colony and Gradient Boosting Decision Tree for Hotel Reservation Cancellation Prediction Using Random Forest

This study focuses on predicting hotel booking cancellations using machine learning to improve accuracy and operational efficiency. The methods used include Random Forest, Artificial Bee Colony (ABC), and Gradient Boosting Decision Tree (GBDT). ABC, which excels in optimization but is prone to local optima, is combined with GBDT for feature selection. The dataset used is Hotel_Bookings from Kaggle, with 119,390 entries and 28 features. The data is processed through cleansing, normalization, and split into 75% for training and 25% for testing. Feature selection is performed using ABC and GBDT, and the prediction model is built using Random Forest. Model evaluation using confusion matrix and metrics like precision, recall, f1-score, and accuracy shows accuracies of 86.17% and 86.65% for ABC and GBDT, respectively. Increasing the number of trees and features generally improves model performance, with feature selection showing significant performance improvements compared to models without feature selection.