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The impact of machine learning models in the prediction of air cargo
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Forecasting is key in the aviation industry. Airlines use software applications to make cargo predictions. This thesis explores the advantages of training Machine Learning models using data from an airline to produce cargo forecasts and it evaluates the quality of these forecasts in the context of the business problem. The dataset available is curated and fed into different regression models, using Python scripts. Results are benchmarked with the current model used by the airline. Most of the models trained in this thesis serve the airline with better cargo predictions than the current model does, at one hour before the scheduled departure time.
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Operations management Business analysis