Prediction of Flight Fares Using Machine Learning

The airline industry has become a very organized sector given the magnitude of travel undertaken by the population these days. However, the airline ticket pricing is a fluid parameter because of its dependency on various factors. The variation in ticket prices has led to customers traveling with different fares even on the same flight. This results in revenue losses for the airline industry due to operation of under booked flights and high customer dissatisfaction. The prediction of flight prices at an early stage would help airlines strategize their operations and gather the necessary resources impacting a specific market segment level for a route. Thus, this paper aims to propose a model which can capture the variability between different factors affecting the cost of a flight ticket and arrive at the model with least mean absolute error for estimating the prices of a trip. The dataset used in this paper has been provided with prices of flight tickets for various airlines between March to June 2019. From the algorithms implemented on the dataset, an ensemble of the Artificial Neural Networks, XGBoost Regressor, and Light Gradient Boosting Machine Regression models gives the least mean absolute error of 1226.6989.