Prediction of New York taxi tip behavior based on machine learning classification and regression methods
- DOI
- 10.2991/978-94-6463-344-3_75How to use a DOI?
- Keywords
- machine learning; taxi prediction; classification; regression
- 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.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Hejingyu Huang PY - 2024 DA - 2024/01/10 TI - Prediction of New York taxi tip behavior based on machine learning classification and regression methods BT - Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023) PB - Atlantis Press SP - 686 EP - 698 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-344-3_75 DO - 10.2991/978-94-6463-344-3_75 ID - Huang2024 ER -