Baptista, Márcia LourençoDamásio, Bruno Miguel PintoLuz, Maria Helena Abreu2026-04-202026-04-202026-04-13http://hdl.handle.net/10362/202374Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementAir travel is widely recognized as one of the safest and most convenient modes of transport worldwide. The aircraft maintenance industry is an important field of operation, therefore itis paramount to find an optimal approach to estimate aircraft tasks durations. This study aims to examine the available literature on existing methods to process data and to estimate duration in aviation. The research comprises an exploratory analysis, with the goal of understanding the real-world maintenance data set and finding relevant hidden insights. The analysis encountered patterns in task efficiency by skill type, aircraft type, and location. The second part of the research conducts an experimental analysis that compares four predictive machine learning models with traditional method PERT (Program Evaluating and Review Technique), a project management technique used to estimate the duration of tasks by considering optimistic, pessimistic, and most likely time estimates. The results of the study demonstrate the value of data-driven approaches in improving accuracy in maintenance task planning and performance.engData AnalyticsMachine LearningTask Duration EstimationAircraft MaintenanceData-Driven Decision MakingMaintenance Repair and Overhaul (MRO)master thesis204297290