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DOI: 10.14569/IJACSA.2022.0130330
PDF

Using Decision Tree Classification Model to Predict Payment Type in NYC Yellow Taxi

Author 1: Hadeer Ismaeil
Author 2: Sherif Kholeif
Author 3: Manal A. Abdel-Fattah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 3, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The taxi services are growing rapidly as reliable services. The demand and competition between service providers is so high. A billion trip records need to be analyzed to raise the spirit of competition, understand the service users, and improve the business. Although decision tree classification is a common algorithm which generates rules that are easy to understand, there is no implementation for classification on taxi dataset. This research applies the decision tree classification model on taxi dataset to classify instances correctly, build a decision tree, and calculate accuracy. This experiment collected decision tree algorithm with Spark framework to present the good performance and high accuracy when predicting payment type. Applied decision tree algorithm with different aspects on NYC taxi dataset results in high accuracy.

Keywords: Big data analytics; apache spark; decision tree classification; taxi trips; machine learning

Hadeer Ismaeil, Sherif Kholeif and Manal A. Abdel-Fattah, “Using Decision Tree Classification Model to Predict Payment Type in NYC Yellow Taxi” International Journal of Advanced Computer Science and Applications(IJACSA), 13(3), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130330

@article{Ismaeil2022,
title = {Using Decision Tree Classification Model to Predict Payment Type in NYC Yellow Taxi},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130330},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130330},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {3},
author = {Hadeer Ismaeil and Sherif Kholeif and Manal A. Abdel-Fattah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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