Numerous companies rely on a number of different airlines to connect them with other regions of the world as a result of the increasingly important position that the aviation industry plays in today's global transportation sector. On the other hand, severe weather can have a direct impact on airline services, most notably in the form of flight delays. The solution to this problem is to accurately predict these flight delays, which enables passengers to be well prepared for the deterrent that will be caused to their journey and enables airlines to respond to the potential causes of the flight delays in advance in order to diminish the negative impact. The goal of this work, which makes use of quantum machine learning, is to investigate the methodologies that are applied in the construction of models for forecasting flight delays that are brought on by inclement weather. In the initial phase of the project, we investigate the feasibility of employing Python-based linear Regression in conjunction with Support Vector Machine. After that, we feed the dataset into our classifier to obtain the results. In the second half of the project, our primary focus is on acquiring data, and we investigate the possibility of combining quantum machine learning with the process of gathering data by first dissecting the dataset and then determining which features are important. After looking over the results, we compared them to the outcomes of other models, such as the machine learning classifier with quantum linear regression and the quantum SVM-SVR, in order to determine which classifier would be the most effective in resolving the issue.

Airline services, delays, mean delay times, quality mean delays and performance matrices.