Disruption is one of the main issues facing the air transport industry. We've all experienced or heard about disruption causing delays, often with huge impacts on passengers, cargo and operations throughout the rest of the day. Let me put it into perspective: recent figures show average flight delay times at 51 minutes. Delays could be costing the air transport industry as much as $25 billion a year. IATA figures also show the scale of the problem for airlines and airports alike.
In the present scenario of domestic flights in USA, there have been numerous instances of flight delays and cancellations. In the United States, the American Airlines, Inc. have been one of the most entrusted and the world's largest airline in terms of number of destinations served. But when it comes to domestic flights, AA has not lived up to the expectations in terms of punctuality or on-time performance. Flight Delays also result in airline companies operating commercial flights to incur huge losses. So, they are trying their best to prevent or avoid Flight Delays and Cancellations by taking certain measures. This study aims at analyzing flight information of US domestic flights operated by American Airlines, covering top 5 busiest airports of US and predicting possible arrival delay of the flight using Data Mining and Machine Learning Approaches. The Gradient Boosting Classifier Model is deployed by training and hyper-parameter tuning it, achieving a maximum accuracy of 85.73%. Such an Intelligent System is very essential in foretelling flights'on-time performance.
Google has updated its Flights app with a pair of new features that should help weary (and wary) travelers get to grips with the next trip to the airport. The first uses machine learning to predict upcoming flight delays, and the second breaks down exactly what different airlines mean by "basic economy" -- explaining what amenities are and are not included in so-called last class.