A Random Forest Regression Approach to Predict Flight Fare

Authors

  • Komal Kalane  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Pune, Maharashtra, India
  • Shivam Ghorpade  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Pune, Maharashtra, India
  • Omkar Jawale  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Pune, Maharashtra, India
  • Abhishek Jaiswal  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Pune, Maharashtra, India
  • Snehal More  Student, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Pune, Maharashtra, India
  • Prof. Monika Dangore  Assistant professor, Department of Computer Engineering, Dr. D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India

Keywords:

Machine Learning Algorithm, Prediction Model, Flight Price, Regression

Abstract

This paper deals with the problem of flight prices prediction. The aviation industry keeps changing the flight prices. Prices on last minute airfare can be highly volatile so customer try to book ticket in advance. To estimate the minimum flight price, data for a specific air route has been collected including the features like departure date, arrival date, source, destination and airways. Features are extracted from the gathered data to apply Machine Learning (ML) Models. Machine Learning regression methods are used to predict the price at the given time. The training set is used to train the algorithm for accurate prediction and this will help to decide a specific airline as per budget.

References

  1. K. Tziridis, K.I. Diamantaras,”Airfare Prices Prediction Using machine Learning Technique”,European signal processing conference 2017
  2. M. Papadakis, “Predicting Airfare Prices,” 2014.
  3. 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 dis-covery and data mining, 2017
  4. 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 Analysis. In: 2013 IEEE 13th International Conference on Data Mining Workshops, Dallas, TX, 2013, pp. 440–444.
  5. 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
  6. Estimating dynamic demand for airlines Econ. Lett., 124 (1) (2014), pp. 26-29
  7. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., “Scikit-learn: Machine learning in python,” Journal of Machine Learning Research, vol. 12, no. Oct, pp. 2825–2830, 2011
  8. A. Ng, “Cs229: Machine learning lecture notes,” Standford University Lecture, 2011.
  9. L. Breiman, J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984
  10. Dominguez-Menchero, J.Santo, Reviera, ”optimal purchase timing in airline markets” ,2014
  11. Abhilash, Ranjana,shilpa and Zubeda’”Survey on Air Price Prediction using Machine Learning Algorithm”, IJIREEICE 2019
  12. L. Breiman, ―Random forests, ‖ Machine Learning, vol. 45, pp. 532, 2001
  13. S.B. Kotsiantis, “Decision trees: a recent overview,” Artificial Intelligence Review, vol. 39, no. 4, pp. 261-283, 2013.

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

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Published

2021-06-30

Issue

Volume 8, Issue 3, May-June-2021

Section

Research Articles

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

How to Cite

[1]
Komal Kalane, Shivam Ghorpade, Omkar Jawale, Abhishek Jaiswal, Snehal More, Prof. Monika Dangore, " A Random Forest Regression Approach to Predict Flight Fare" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.321-324, May-June-2021.