Manuscript Number : IJSRST205844

Flight Ticket Price Prediction Using Machine Learning

Authors(5) :-Komal Kalane, Shivam Ghorpade, Omkar Jawale, Snehal More, Prof. Monika Dangore

Nowadays, airline ticket prices can vary dynamically and significantly for the same flight, even for nearby seats within the same cabin. Customers are seeking to get the lowest price while airlines are trying to keep their overall revenue as high as possible and maximize their profit. Airlines use various kinds of computational techniques to increase their revenue such as demand prediction and price discrimination. From the customer side, two kinds of models are proposed by different researchers to save money for customers: models that predict the optimal time to buy a ticket and models that predict the minimum ticket price. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. Features extracted from external factors such as social media data and search engine query are not considered. Therefore, we introduce and discuss the concept of using social media data for ticket/demand prediction.

Authors and Affiliations

Komal Kalane
Computer Department, Savitribai Phule Pune University, D.Y. Patil School of Engineering Charholi, Pune, Maharashtra, India
Shivam Ghorpade
Computer Department, Savitribai Phule Pune University, D.Y. Patil School of Engineering Charholi, Pune, Maharashtra, India
Omkar Jawale
Computer Department, Savitribai Phule Pune University, D.Y. Patil School of Engineering Charholi, Pune, Maharashtra, India
Snehal More
Computer Department, Savitribai Phule Pune University, D.Y. Patil School of Engineering Charholi, Pune, Maharashtra, India
Prof. Monika Dangore
Computer Department, Savitribai Phule Pune University, D.Y. Patil School of Engineering Charholi, Pune, Maharashtra, India

Survey, Ticket price prediction, Demand prediction, Price discrimination, Social media

  1. A. Mottini and R. Acuna-Agost, “Deep choice model using pointer networks for airline itinerary prediction,” in the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017
  2. T. Liu, J. Cao, Y. Tan, and Q. Xiao, “ACER: An adaptive context-aware ensemble regression model for airfare price prediction,” in the international conference on progress in informatics and computing, 2017
  3. C. Koopmans and R. Lieshout, “Airline cost changes: To what extent are they passed through to the passenger?” Journal of Air Transport Management, vol. 53, pp. 1–11, 2016.
  4. P. Malighetti, S. Paleari and R. Redondi, “Pricing strategies of low-cost airlines: The Ryanair case study,” Journal of Air Transport Management, vol. 15, no. 4, pp. 195-203, 2009.
  5. A regression model for predicting optimal purchase timing for airline tickets William Groves and Maria Gini October 18, 2011
  6. Predicting Airfare Prices Manolis Papadakis, 2014
  7. Linear Quantile Mixed Regression Model for Prediction of Airline Ticket Prices, 2014
  8. To Buy or Not to Buy: Mining Airfare Data to Minimize, 2003
  9. Random Forests, January 2011
  10. Dingli, A., Mercieca, L., Spina, R., Galea, M., 2015. Event detection using social sensors. In: 2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Rennes, 2015, pp. 35–41.
  11. Porshnev, A., Redkin, I., Shevchenko, A., 2013. Machine Learning in Prediction of Stock Market Indicators Based on Historical Data and Data from Twitter Sentiment Analysis. In: 2013 IEEE 13th International Conference on Data Mining Workshops, Dallas, TX, 2013, pp. 440–444.
  12. Bo An, Haipeng Chen, Noseong Park, V.S. SubrahmanianMAP: Frequency-Based Maximization of Airline Profits based on an Ensemble Forecasting Approach Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), ACM, New York, NY, USA (2016), pp. 421-430
  13. Bhavuk Chawla, Ms Chandandeep KaurAirfare Analysis And Prediction Using Data Mining And Machine Learning Int. J. Eng. Sci. Invention, 6 (11) (2017), pp. 10-17
  14. Chieh-Hua Wen, Po-Hung ChenPassenger booking timing for low-cost airlines: a continuous logit approach J. Air Transport Manage., 64 (2017), pp. 91-99
  15. Estimating dynamic demand for airlines Econ. Lett., 124 (1) (2014), pp. 26-29

Publication Details

Published in : Volume 5 | Issue 8 | November-December 2020
Date of Publication : 2020-12-18
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 260-264
Manuscript Number : IJSRST205844
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Komal Kalane, Shivam Ghorpade, Omkar Jawale, Snehal More, Prof. Monika Dangore, " Flight Ticket Price Prediction Using Machine Learning", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 5, Issue 8, pp.260-264, November-December-2020.
Journal URL : https://ijsrst.com/IJSRST205844
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