<![CDATA[In aviation, “no-show†refers to a customer who booked a reservation but failed to show up. No-shows can result in various resource wastes, such as vacant seats, leading to income loss and flight delays. As a result, no-show passengers can cause considerable problems for airlines, ultimately affecting their bottom line. Recent research has shown the use of machine learning algorithms to reduce the rate of no-shows. For example, a researcher in healthcare is using a predictive model to identify no-shows’ patients to increase efficiency. Therefore, this study aimed to develop prediction models to predict passenger no-shows. In this work, we used a dataset supplied by a local airline company consisting of 1,046,486 rows and 8 columns. Additional datasets like weather data, public holiday data of different countries, aircraft details, and foot traffic data are used to carry out the dataset's feature enrichment task to complement the original dataset. As a result, feature selection has become an important stage in this research to identify and pick the most relevant and useful features from the enormous number of columns. The findings showed that the model built using Random Forest has the highest accuracy of 90.4%, while Decision Tree performed at 90.2%, Gradient Boosting at 86.5%, and Neural Networks at 67.6%. To enhance the accuracy of the models, further research efforts are essential to integrate supplementary passenger information.]]>
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