FLIGHT FARE PREDICTION USING MACHINE LEARNING

Abstract

 The Flight Fare Prediction System is a comprehensive solution aimed at accurately forecasting flight ticket prices, providing travelers with valuable insights for better planning and decision-making. Nowadays, airline ticket prices can vary dynamically for the same flight. From the customer's perspective, they want to save money, so I have proposed a model that predicts the approximate ticket price. This system leverages machine learning algorithms and historical flight data to generate accurate fare predictions. The system utilizes a vast dataset comprising historical flight fares, including factors such as travel dates, destinations, airlines, departure times, and various other relevant variables. By analyzing this data using advanced machine learning techniques, the system learns patterns and relationships, enabling it to make reliable predictions about future flight fares. An ensemble of machine learning algorithms, including regression-based models like Random Forest, Gradient Boosting, and Support Vector Regression, is employed to capture complex patterns and relationships within the data. This system will give people an idea of the trends the prices follow and also provide the predicted value of the price, which they can check before booking flights to save money. This kind of system or service can be provided to customers through flight booking companies to help them book tickets.