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Airline dynamic price prediction using machine learning

Author

Listed:
  • Kushal Kumar Ruia
  • Utkarsh Daga
  • Aditya Tripathi
  • Maruf Nissar Rahman
  • Saurabh Bilgaiyan

Abstract

Nowadays, to optimise revenue, airlines use dynamic pricing techniques. Earlier demand was predicted by retrospective analysis of sales data across various sales channels. This technique becomes less reliable when the volatility of the market and competition increases. In this paper, different models have been built using machine learning techniques like regression which could predict the ticket prices of airlines based on features such as journey route, historical ticket price, etc. Along with this, the dependency of important features on accuracy has also been studied. An experimental study of this paper reveals that regression algorithms can be used to predict airline price as the highest R2 score achieved on testing data is 0.85 using gradient boosting regressor. The objective of this paper is to understand the key features that affect the airline price and compare the accuracy of the different machine learning algorithms with the selection of different features.

Suggested Citation

  • Kushal Kumar Ruia & Utkarsh Daga & Aditya Tripathi & Maruf Nissar Rahman & Saurabh Bilgaiyan, 2022. "Airline dynamic price prediction using machine learning," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 36(2), pages 187-207.
    • Handle: RePEc:ids:ijpqma:v:36:y:2022:i:2:p:187-207
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