Big Data Visualization and Analysis of Various Factors Contributing to Airline Delay in the United States

Airline delay analysis aims to evaluate which factors most likely contribute to airline delays, which can help airline companies avoid airline delays and make better flight planning to minimize their loss. Flight delay is attributed to various factors, such as bad weather conditions, physical flaws, delayed flight arrivals, and crew-related issues. With the help of Big Data visualization and analysis, various factors contributing to airline delays in the United States are investigated to make suggestions for airline companies. First, this paper pre-processes the dataset by restricting the time period to rule out the influence of Covid-19 on the irregularity of airline schedules. To obtain the most accurate results, we expurgate missing data. Then, this paper visualizes the correlations between various factors, including months, airline carriers, departure times, weather conditions, and airline delay rate. Each factor is assigned as strong, weak, or no correlation with airline delay. Next, this paper evaluates the results by comparing findings from other research or reports, which support the result from the study. Finally, the optimal situation is analyzed for a flight to depart on time, and suggestions are provided for airline companies to reduce their delay rate. Our analysis shows that the correlation between airline carriers, departure times, and airline delay is strong. There is a weak correlation between weather conditions and airline delays. There is no correlation between months and plane age to an airline delay.