Dynamic Airline Pricing System

Competition over fare control has reached a new level of complexity in the airline industry through machine learning to determine the most effective ticket pricing strategies. This research paper demonstrates an ideal dynamic pricing model developed on the programming language Python including preprocessing of data, selection of features and other state of the art models Random Forest and Prophet model among others. The model takes data flights details, economic conditions, weather conditions, and customers demographics of the flight to predict ticket prices correctly. Due to the interface, implemented with Streamlit, the model enables users to input numerous parameters and obtain flight price estimations. The results clearly bring out the possibility of the use of machine learning in the airline industries to improve the revenue management and therefore increase the right price solution and customer satisfaction. This paper seeks to add on the existing literature pertaining to ERP and Advance Metering Infrastructure and its implementation in Airline Industry.