Predictive Model for Airline Fare Estimation

Abstract

travel planning, offering travelers valuable insights into the best times to purchase tickets at optimal prices. This project leverages machine learning techniques to create a system that analyzes historical flight data, seasonal trends, and real-time variables to predict airfare prices accurately. Unlike traditional rule-based and static pricing models, which often fail to adapt to dynamic market conditions, this approach uses data-driven algorithms to offer personalized and timely fare predictions. The result is a smart solution that empowers users to make cost-effective travel decisions with minimal effort. The proposed system is implemented as a user-friendly web application, integrating backend machine learning models with intuitive frontend design. Users can enter travel details such as source, destination, date, and airline preferences, and the system returns a real-time fare prediction. Key modules include user input, data preprocessing, prediction generation, and result display, all supported by technologies such as Python, PyTorch, Flask, and Scikit-learn. This solution not only simplifies the flight booking experience but also enhances affordability and transparency for users by minimizing the guesswork traditionally involved in fare comparisons.