PREDICTIVE MODEL FOR AIRLINES’ FLIGHT DELAY & PRICING

Abstract [English]

Now-a-days Airline ticket prices and delays in the flight have become unpredictable. Ticket prices are dynamic and change significantly for the same flight and even for the same class of seat. Airline companies implements various algorithms to change the prices dynamically, so as to maximize their revenue. Because of tough competition among airline services these models are not available to the general public. Also, the flight gets delayed because of various micro and macro factors. The major factors that affect the airlines are air route situation, delay of previous flight, aircraft capacity, air traffic control, airline properties, etc. There is a need to predict the flight delays and flight prices of airlines to save both ‘Time and Money’. We are building a platform for airplane commuters to predict the flight delays and flight prices. Using this tool, they will be able to plan their travel efficiently and thereby save money. The interface of the tool will be user-friendly. We will be applying various machine learning algorithms to predict the prices and delays, and implement the most efficient and effective algorithms in the tool. Our system will comprise of two main components namely price prediction module and delay prediction module.