Conclusion

Upon delving deeper into the weather data to identify any monthly trends, it was found that both the average origin and destination weather delays were highest in December. This spike is primarily due to cold weather and ice storms in many areas which occur during the month of December. Typically, ice storms result in significant flight delays, with wait times in December notably longer than in other months. Additionally, a correlation analysis was conducted to determine if weather delays influence other types of delays, such as NAC delays and security delays. However, no significant relationship was found between weather delays and these other types of delays. Further examination of state-specific data revealed that North Dakota and Idaho experience the highest average weather delays, approximately 1.7 hours. Geographical analysis suggests that the cold climates of these states contribute to their elevated average weather delays.

After conducting a thorough Exploratory Data Analysis (EDA), unsupervised machine learning techniques were employed to uncover patterns within the data. Clustering methods initially divided the data into two to three clusters based on temperature, but this did not yield informative patterns. However, the results from Association Rule Mining (ARM) were more revealing, which produced several surprising insights. Contrary to common belief, the ARM revealed that flight delays are more frequent in warm temperatures than in cooler or cold conditions. This finding challenges the assumption that colder temperatures, often associated with ice and snow, are the primary cause of flight delays. Further analysis clarified that in warm temperatures, the air becomes less dense, which adversely affects aircraft performance. Additionally, warm conditions often bring increased humidity and the possibility of rain, both of which can lead to further delays. Issues such as mid-air turbulence and reduced visibility are also more prevalent in warmer weather, contributing to these delays. 

Another significant insight from using ARM with multiple features was the identification of airlines and states with high incidences of delays. It was found that Southwest Airlines, Spirit Airlines, and Envoy Airlines commonly experience delays. Geographically, states like Florida, Texas, Illinois, and North Carolina see a higher frequency of delays. Notably, Envoy Air, based in the colder climate of Illinois, often experiences delays during cold weather. In contrast, Spirit Airlines, located in Florida’s warmer environment, encounters considerable delays during hot periods. These findings from unsupervised machine learning techniques such as ARM provide valuable insights into the complexities of flight delays, illustrating how both environmental conditions and operational contexts influence airline performance.

Following that various supervised machine-learning models, like Decision Trees, Naïve Bayes, and SVM, were applied to predict weather delays based on the temperature and the state of origin and destination. However, the performance of these models was unsatisfactory, as evidenced by low accuracy and other classification metrics like the F1 score. From these outcomes, three potential conclusions can be drawn:

1. The models may not have been capable of capturing the relationship between temperature and weather delays effectively.

2. There might not be a significant relationship between temperature alone and weather delays.

3. Temperature is likely not the sole factor influencing weather delays; other variables such as humidity, dew point, and wind speed could be crucial in enhancing the prediction of weather-related delays. 

These insights suggest that a more comprehensive approach, incorporating additional meteorological factors, might be necessary to improve the predictive accuracy of weather delays in future models.