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Flight Delay Prediction using Machine Learning

Author(s):

Shashant Pandit , Rajiv Gandhi College of Engineering Research and Technology, Chandrapur; Chetan Khobragade, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur; Devyani Shrikundwar, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur; Abhishek Dahikar, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur; Prof.Manisha Pise, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur

Keywords:

Flight Delay Prediction, Machine Learning

Abstract

Flight delays are quite frequent (19% of the INDIA domestic flights arrive more than 30 minutes late), and are a major source of frustration and cost for the passengers. As we will see, some flights are more frequently delayed than others, and there is an interest in providing the information to travelers. As delays are a stochastic phenomenon, it is interesting to study their entire probability distribution, instead of looking for an average value. This master’s thesis proposes models to estimate delay probability distribution, based on a method called kernel density estimation and its extensions. These are data-driven methods, meaning that it does not try to model the underlying processes, but only consider past observations. Our models of increasing complexity have been implemented, optimized and evaluated on a large scale, using several years of records of INDIA domestic flights delays. During evaluation, we will measure the good performance of some of the models to predict delay distributions, in of the intrinsic difficulty of measuring the goodness of fit between a probability distribution and the corresponding random experiment.

Other Details

Paper ID: IJSRDV8I30457
Published in: Volume : 8, Issue : 3
Publication Date: 01/06/2020
Page(s): 496-497

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