PREDICTION OF FUTURE FLIGHT DELAYS BASED ON CURRENT DATA ANALYSIS USING MACHINE LEARNING

Abstract

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay)

The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems