This deep learning model analyzes flight data collected from Kaggle. The data was cleaned and
visualized to gain insights into airport and airline flight information. The top 10 destination flights
from NYC were identified along with the maximum number of flights headed to unique
destinations from the origin. The total number of unique airlines headed to BOS from NYC was
also determined.
The model then focused on delay information and classified departure and arrival delay
statuses. Average monthly departure delays for carriers were calculated, and the top 10 arrival
delays for destinations with a sample size greater than 1000 were identified. Additionally, the
average arrival and departure delays were determined by carrier, and the day and month with
the highest number of flight delays were analyzed. Finally, histograms were created to visualize
arrival and departure delays.
The model also analyzed on-time departure and arrival data and provided a performance
analysis. The findings of this study can be used to improve the efficiency of airport and airline
operations, resulting in a better travel experience for passengers.
Fig: The peak season for air travel in USA is considered to be June to August and lean season is mid of January to February. The airlines operate highest number of flights and carry maximum PAX load during the summer season and vis-à-vis during lean season. The data proves that the statement is true and most of the airlines having maximum departure between May to August and minimum between January to February. From, the heatmap, it is visible that during May to August most of the airlines tend to fly faster than normal flight speed, to cover maximum departure. Whereas, it is vis-à-vis during lean season.
Fig: The coorelation matrix provide the result that the departure and arrival delay are positively and strongly coorelated. Similarly with air time and flight distance; So they tend to move in the same direction. Whereas correlation between fightspeed, airtime, flightdistance are positive but not so strong enough to influence each other.