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nyc-taxi-dataset

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I'm attempting the NYC Taxi Duration prediction Kaggle challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook.…

  • Updated Sep 21, 2018
  • Jupyter Notebook

In this project using New York dataset we will predict the fare price of next trip. The dataset can be downloaded from https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips The dataset contains 2 Crore records and 8 features along with GPS coordinates of pickup and dropoff

  • Updated Jun 13, 2019
  • Jupyter Notebook

NYC Taxi Fare Prediction with 7 models (Linear Regression, Random Forest, XGBoost, LightGBM, CatBoost, KNN, and Decision Tree) The models used range from simple linear regression to more complex ensemble methods such as boosting algorithms. The aim was to improve prediction accuracy and handle categorical features efficiently.

  • Updated Feb 14, 2023
  • Jupyter Notebook

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