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Prediction of New York taxi tip behavior based on machine learning classification and regression methods

In: Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023)

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  • Hejingyu Huang

    (Beijing Forestry University, Geographic Information Science at)

Abstract

In the context of machine learning, this study employs a learning method to process big data and predict and analyze taxi tip behavior. Basic variables such as trip time, trip distance, and number of passengers are added to the dataset, as well as special variables related to geographic location. Using these variables, a two-stage model is created in which a random forest classification model with an accuracy rate of 98.3% in the first stage and a Lasso regression model with an MSE value of 0.007294 in the second stage are used to predict taxi tip behavior, resulting in better fitting than a single model prediction.

Suggested Citation

  • Hejingyu Huang, 2023. "Prediction of New York taxi tip behavior based on machine learning classification and regression methods," Advances in Economics, Business and Management Research, in: Zhikai Wang & Qiujing Wu & Songsong Liu & Guoliang Wang & Jia Li (ed.), Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023), pages 686-698, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-344-3_75
    DOI: 10.2991/978-94-6463-344-3_75
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