Conclusions

In this study, the relationship between flight delays and temperature conditions at both origin and destination airports was explored using a mathematical machine learning model SVM. The aim was to determine if temperatures alone could predict delays. However, the results did not meet expectations, indicating that temperatures by themselves might not be a reliable indicator of delays. 

Three main reasons appear to explain the unsatisfactory outcome:

• Inadequacy of the Model: It is possible that the model used was not capable of capturing the complexities of how temperatures affect flight delays.

• Relevance of Temperature: There may be a weak or nonexistent direct link between temperature and the likelihood of delays.

• Other Influential Factors: The study overlooked additional weather elements such as humidity, dew point, and wind speed, which could also significantly impact flight delays.

Considering these findings, future research should expand the range of weather conditions analyzed. Incorporating a broader set of features might improve understanding of what causes flight delays and enhance the accuracy of predictions.