Airline Analytics

Singaravelu, Devapriya (2014) Airline Analytics. Diploma thesis, Dublin, National College of Ireland.

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This research works describes about predicting the U.S flight delay using various statistical modelling and Data mining Techniques. This research of U.S Airline data of the Federal Aviation administration (FAA) used to analyse various statistical modelling and Data Mining techniques such as cumulative distribution of the datasets, Logistic regeression,Anova,Time series analysis-means algorithm ,Linear regression,randomforest,have been implemented to detect the patterns of the flight delay such as the Arrival delay, carrier delay, weather delay, Departure delay and the schedule performance of the Airlines. These delay of the airline also have been discussed in the Literature review paper.

The results of the research shows the prediction of the arrival delay in a airport due to weather calculating the arrival time and the air time of the flights. The models shows the prediction of the departure delay in a airport due to the security delay calculating the departure time of the origin of the airport in which the data consist of several origin and destination. The arrival delay has been analysed as it may be due to the weather and estimated the delay in weekly patterns and monthly patterns. This paper also shows the cumulative distribution of the flights travelling in a month, week and its frequency count showing some histograms and barplots. Airlines may be delayed due to inadequate planning,carrier delay so In this research we also predicted the departure delay and arrival delay in minutes using the logistic regression. The weather and security delay may be the significant factors for the arrival delay and departure delay.

Item Type: Thesis (Diploma)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Higher Diploma in Science in Data Analytics
Date Deposited: 15 Dec 2014 10:45
Last Modified: 15 Dec 2014 10:46

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