With ever increasing air route connectivity throughout the world, air travel has become a common, integral and faster
way to travel. Predicting fares for airlines is an important as well as challenging task since a constant fluctuation in fares is
observed and it is known to be dependent on varied set factors. With tremendous study in area, it is observed that using Machine
Learning, Artificial Intelligence and Deep Learning techniques an estimation of flight fares at a given time can be obtained
within seconds. In this paper, we use a Machine Learning Regression approach to predict flight fare by providing basic details of
departure date and time, arrival time, source, destination, number of stops and name of the airline. The results show that
Random Forest Regression Model provides highly optimal results.
