taxi-data
Here are 25 public repositories matching this topic...
This repository houses complete data science projects.
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Jun 6, 2023 - Jupyter Notebook
About 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 8 features along with GPS coordinates of pickup and dropoff
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Aug 13, 2022 - Jupyter Notebook
🚕 M.EIC 2021/2022 - 1ˢᵗ year/ 2ⁿᵈ semester
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Oct 3, 2023 - C++
Taxi analysis for the New York City area with a focus on Manhattan
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Dec 5, 2023 - Jupyter Notebook
Scenario creation for amodeus taxi scenarios, initially private, eventually public
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Apr 14, 2023 - Java
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Apr 26, 2020 - Go
Used Python to draw interactive bar chart and piechart with NYC taxi data
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Apr 1, 2023 - Jupyter Notebook
Explore NYC Green Taxi data, predicting fares and optimizing pickup locations using machine learning. Regression models uncover travel patterns and enhance taxi services for an efficient urban transport experience.
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Jan 19, 2024 - Jupyter Notebook
New York Taxi dataset analysis using Python
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Dec 8, 2022 - Jupyter Notebook
Map-Reduce jobs in python to get insightful information from NYC Taxi data
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May 4, 2018 - Python
Partner api to order taxi service - kiwitaxi
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May 13, 2017 - Go
Create an animation from the NYC Taxi dataset
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Nov 12, 2020 - Python
Code for fetching, sampling, and analysis of NYC taxi data from TLC and Uber for 2009-2018
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Oct 26, 2020 - 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
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Jun 13, 2019 - Jupyter Notebook
Scripts to the build a balanced panel of the 2013 NYC Taxi Data
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Apr 2, 2020 - R
Project to predict the duration of a taxi trip in NYC.
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Jan 22, 2024 - Jupyter Notebook
Using NYC Taxi Data as a Predictor of Urban Air Quality
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Sep 7, 2016 - Python
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