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.

翻译：本文探讨了利用R平方（R²）和均方根误差（RMSE）对航班定价数据进行预测性能及处理过程的研究，所采用的数据集来自Expedia.com，包含约2000万条记录（约4.68GB）。该项目旨在确定现实中可用于预测美国境内直飞航班票价的最佳模型，因此模型需具备良好的泛化能力和优化的处理时间。我们将通过特征重要性发现关键商业洞察，并讨论分析过程及所用工具。本研究采用了四种回归机器学习算法：随机森林、梯度提升树、决策树和因子分解机，并利用交叉验证器与训练验证器函数评估其性能及泛化能力。