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Airline ticket prices can vary dramatically for flights in the same sector and from the same airlines depending on the festive seasons. Last year, during festival season, to facilitate festive commuters, domestic airline companies had increased flights to many different destinations. In October of 2019, air transport operators said that though the flights from Kathmandu to other destinations are booked to 100 per cent capacity, flights return to Kathmandu at less than 20 percent occupancy. Nearly 2.5 million people were expected to leave Kathmandu during Dashain.[1] The difference in the price of flight tickets is affected by other factors like the length of the flight route and the difference in booking date and flight date. This paper aims to study the correlation and interactions among different attributes of an airfare price using data mining and machine learning techniques and answer some questions related to airfare price using statistical analysis based hypothesis testing methods.