Predicting Flight Prices by using Neural Networks and Regression

Abstract

The objective of the Flight Fare Prediction System is to help travellers plan their trips and manage their finances by offering accurate estimates of flight ticket prices. Frequent variations in airfare for the same flight often pose challenges for securing economical options. This predictive model resolves such issues by estimating ticket prices, helping users save money and understand price trends better. The strategy makes use of sophisticated machine learning techniques and historical flight data to ensure dependable fare predictions. Key factors, such as departure times, airlines, travel dates, destinations, and other pertinent information and analysed to discern patterns influencing pricing.

The system incorporates a number of machine learning strategies, such as Random Forest, Gradient Boosting, and Support Vector Regression. By delivering fare estimates and insights into pricing trends, this system enables users to make informed booking choices. Its integration into flight booking platforms provides a useful resource for travellers to find cost-effective options and plan trips with ease.