Airline Data Analysis

Authors

  • Navuluri Madhavilatha  Guest Faculty, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
  • Bheema Shireesha  Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
  • Chunduru Anilkumar  Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India

DOI:

https://doi.org//10.32628/CSEIT19514

Keywords:

Machine Learning, Data Mining, Big Data, Statistics, Data Visualization, Data Analytics, ASCII, SRS, GNU

Abstract

In the contemporary world, Data analysis is a challenge in the era of varied inters disciplines through there is a specialization in the respective disciplines. In other words, effective data analytics helps in analyzing the data of any business system. Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision-making process for all players of commercial aviation. The goal of our project is to get monthly wise statistics of airline data and taking particular airport as target we are further analyzing the data to get the hourly statistics. And also we are finding out the most popular source-destination pairs and calculating the average delays at every airport. The data for this project comes from the stat-computing.org website. In particular, in the year 2008 data 70,09,728 titles recorded there which includes information on the Origin, Destination, Month, Year, DayofWeek, DayofMonth, DepDelay, ArvDelay, DepTime, ArvTime and a few other less interesting variables. Conveniently, you can export the data directly as a csv file.

References

  1. Abdelghany, K. F., Abdelghany, A. F., and Raina S., (2004) A model for projecting flight delays during irregular operation conditions, Journal of Air Transport Management, Volume 10, Issue 6, Pages 385-394.
  2. American Statistical Association(stat-computing.org)
  3. Thearling, K., Data Mining and Analytic Technologies, www.thearling .com, 2004.
  4. Tutorials point (http://www.tutorilaspoint.com/r/)
  5. Aisling, R., and J.B. Kenneth, (1999) An assessment of the capacity and congestion levels at European airports, ERSA conference papers ersa 99, pages 241, European Regional Science Association
  6. Bureau of Transportation Statistics, Airline On-Time Statistic. U.S. Department of Transportation. Washington, D.C. http://www.bts.gov/programs/airline information
  7. Hansen, M., and C. Y. Hsiao (2005), Going South? An Econometric Analysis of US Airline Flight Delays from 2000 to 2004, Presented at the 84rd Annual Meeting of the Transportation Research Board (TRB), Washington D.C., 2005.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2019-01-30

Issue

Volume 5, Issue 1, January-February-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Navuluri Madhavilatha, Bheema Shireesha, Chunduru Anilkumar, " Airline Data Analysis, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.22-29, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT19514