Dynamic Flight Price Prediction Using Machine Learning Algorithms

In terms of convenience in Mode of Transportation, Airplane has become one of the primary methods for long as well as short-distance travel. But flight prices are dynamic and to predict them for normal people is so hectic. As the price keeps on changing on the demand, flight prices are different during morning time and evening time of the day. It also keeps on changing by seeing the holidays as well as the special festive season. The price of flight tickets gets affected by so many factors. Most distributors have data or information on many parameters, but purchasers only have limited knowledge, which is insufficient to anticipate flight prices. In this paper, we will explore a machine learning model that will take into account features such as departure location, number of stops, departure time, arrival time, the number of days till departure, and time of day to determine the optimal time to purchase tickets. The goal of this research work is to identify the elements that influence changes in travel prices and how these factors are linked to changes in values.