Flight Fare Prediction Using Random Forest Regression

Flight Fare Prediction Using Random Forest Regression

Prerna Jhingran, Jaspreet Singh, Rajni Danu, Harshita Singh, Ankur Srivastava

Student, Student, Student, Student, Assistant Professor

Computer Science and Engineering,

Babu Banarasi Das Institute of Technology and Management, Lucknow, India

Abstract

The deflection in the rates of the flight ticket is an everyday deed and there are many reasons behind this like destination, time, duration etc. Each carrier has its own proprietary rules and algorithm to set the price accordingly. Advances in artificial Intelligence and Machine Learning makes it possible to infer such rules and model the price validation. This review paper consists of several year data for the prediction of the airfare.

Customers want airfare to be minimum while airline companies want to make the maximum profit possible.The project implements the validations or contradictions towards myths regarding the airline industry, a comparison study among various models in predicting the optimal time to buy the flight ticket and the amount that can be saved if done. A customized model which included a combination of ensemble and statistical models have been implemented with a best accuracy of above 90%

For a few routes , mostly from Tier 2 to metro cities. As a result, having a basic understanding of flight rates before booking a vacation will undoubtedly save many individuals money and time.Remarkably, the trends of the prices are highly sensitive to the route, month of departure, day of

departure, time of departure, whether the day of departure is a holiday and airline carrier. Highly competitive routes like most business routes (tier 1 to tier 1 cities like Mumbai-Delhi) had a non-decreasing trend where prices increased as days to departure decreased, however other routes (tier 1 to tier 2 cities like Delhi - Guwahati) had a specific time frame where the prices are minimum. Moreover, the data also uncovered two basic categories of airline carriers operating in India – the economical group and the luxurious group, and in most cases, the minimum priced flight was a member of the economical group. The data also validated the fact that, there are certain time-periods of the day where the prices are expected to be maximum.

Key Words: Random forest Regression, Flight Fare Prediction, Linear Regression, Machine Learning