Description

Air travel is the fastest transportation network in the globe, and it has a great impact on the economy for helping in economic growth, reducing poverty and making employment for millions of people. This increase in using Ćight travel made it harder for airline companies to maintain a perfect airfare pricing that should be fair for both: companies to make a nice revenue of it and passengers to enjoy their travel without money issues. But this is a hard task due to multiple conditions that the Ćight price depends on and the consecutive changing of these prices make it even harder to evaluate. The travel industry is evolving quickly, drawing in an ever-increasing number of vacationers consistently. The carrier business is viewed as one of the world’s quickest developing ventures, with a solid estimating methodology. Since the airline company’s privatization, the airfare pricing scheme has evolved into a complex framework of sophisticated regulations including numerical simulations that determine airfare marketing strategies. The system is developed the different machine learning algorithms such as Linear Regression and Random Forest Regression for forecasting the price of airline effectively. Then, finally the system can estimate some performance metrics such as R-Squared and Accuracy for both algorithms.