Airfare Analysis And Prediction Using Data Mining And Machine Learning
Journal Title: International Journal of Engineering and Science Invention - Year 2017, Vol 6, Issue 11
Abstract
As domestic air travel is getting more and more popular these days in India with various air ticket booking channels coming up online, travelers are trying to understand how these airline companies make decisions regarding ticket prices over time. Unfortunately this dynamic pricing strategy is usually carried out programmatically and is based on certain hidden parameters (e.g. number of days left till flight departure, or number of seats left). The paper works on mining the previous airfare data and developing data modeling technique to predict the price variation over time so that the consumer could benefit from it. This paper documents a study conducted to understand the airfare dependency over many hidden variables of which oil price, week day of departure, number of stops still have not received much attention from the research community, it also describes the two different methodologies adopted to model this price change, comparative analysis of algorithms under these two methodologies, applied on real world data has also been performed. The comparative analysis thus helped us to find out the most effective algorithm for the prediction of the airfare variations. The study suggests that mining historical airfare data and modeling using machine learning algorithms can help predict price trend and save consumers substantial sum of money.
Authors and Affiliations
Bhavuk Chawla, Ms. Chandandeep Kaur
Keywords
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