Abstract: Flight delays have been extensively studied in recent years. The rising demand for air travel has led to a rise in flight delays. Commercial scheduled flights regularly encounter delays as a result of clogged airspace, a rise in passengers each year, maintenance and safety concerns, unfavourable weather, and the delayed arrival of the aircraft that will be used for the next flight. In order to considerably reduce expenses, academics are looking at how to anticipate and analyse flight delays because it has become a serious problem in the US. The recommended approach therefore makes use of machine learning to predict flight arrival and delay. We have developed a model that implements different machine learning algorithms to predict whether a flight will be delayed or not based on certain characteristics. These characteristics include weather data, past flight data and flight details. We have analysed numerous algorithms based on past research and settled on the Support Vector Machine or the SVM algorithm. The SVM algorithm is a supervised machine learning algorithm which is majorly used for classification as well as regression problems. We also aim to help passengers in their stay in the vicinity of the airport in situations where their flights are delayed.

Keywords: Flight delay prediction, Supervised Machine Learning, Classification, Prediction, Support Vector Machine, Air traffic management, predictive analytics.

PDF | DOI: 10.17148/IJARCCE.2023.125188

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