Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data

TL;DR

This paper evaluates various machine learning models on a large dataset to predict US airline ticket prices, focusing on accuracy, processing efficiency, and business insights.

Contribution

It compares four regression algorithms using cross-validation techniques to identify the most effective model for real-world flight fare prediction.

Findings

Random Forest achieved the best predictive performance.

Gradient Boosted Trees showed strong generalization capabilities.

Feature importance analysis revealed key factors influencing ticket prices.

Abstract

This paper discusses predictive performance and processes undertaken on flight pricing data utilizing r2(r-square) and RMSE that leverages a large dataset, originally from Expedia.com, consisting of approximately 20 million records or 4.68 gigabytes. The project aims to determine the best models usable in the real world to predict airline ticket fares for non-stop flights across the US. Therefore, good generalization capability and optimized processing times are important measures for the model. We will discover key business insights utilizing feature importance and discuss the process and tools used for our analysis. Four regression machine learning algorithms were utilized: Random Forest, Gradient Boost Tree, Decision Tree, and Factorization Machines utilizing Cross Validator and Training Validator functions for assessing performance and generalization capability.