| Summary: | This study aims to identify the main factors leading to charter flight departure delay through data mining. The data sample analysed consists of 5,484 flights operated by a European airline between 2014 and 2017. The tuned dataset of 33 features was used for modelling departure delay (e.g., if the flight delayed more than 15 minutes). The results proved the value of the proposed approach by an area under the receiver operating characteristic curve of 0.831 and supported knowledge extraction through the data-based sensitivity analysis. The features related to previous flight delay information were considered as being the most influential toward current flight being delayed or not, which is consistent with the propagating effect of flight delays. However, it is not the reason for the previous delay nor the delay duration that accounted for the most relevance. Instead, a computed feature indicating if there were two or more registered reasons accounted for 33% of relevance. The contributions include also using a broader data mining approach supported by an extensive data understanding and preparation stage using both proprietary and open access data sources to build a comprehensive dataset.
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