Import public NYC taxi and for-hire vehicle (Uber, Lyft) trip data into a PostgreSQL or ClickHouse database
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Apr 1, 2024 - R
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nyc-taxi-dataset
Here are 70 public repositories matching this topic...
Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data
traffic dataset data-collection traffic-data spatio-temporal-data traffic-prediction grid-data traffic-flow nyc-taxi-dataset nyc-taxi nyc-bike tdrive traffic-dataset taxibj
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Jun 30, 2021
Analyzing 200 GB of NYC taxi dataset.
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Nov 13, 2017 - Jupyter Notebook
Design/Implement stream/batch architecture on NYC taxi data | #DE
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Apr 29, 2021 - Scala
Develop ML models predict taxi trip duration in NYC. Ranked : Top 6% | RMSLE : 0.377 (Kaggle) | #DS
python java data-science machine-learning spark sklearn geolocation kaggle pyspark spark-mllib nyc-taxi-dataset nyc-taxi
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Jan 7, 2023 - Jupyter Notebook
Predict NYC taxi travel times (Kaggle competition)
python random-forest linear-regression kaggle-competition xgboost feature-engineering knn-regression nyc-taxi-dataset fastest-routes visualization-image
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Nov 3, 2017 - Jupyter Notebook
A time-series, regression problem to find the number of pickups, given coordinates in NYC.
graphviz timeseries numpy sklearn regression pandas seaborn xgboost matplotlib pickle dask folium graphviz-source toolz nyc-taxi-dataset gpxpy folium-maps graphvi cloudpickle
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Oct 7, 2020 - Jupyter Notebook
Final Project for the 'Machine Learning and Deep Learning' Course at AGH Doctoral School
deep-learning time-series autoencoder anomaly-detection nyc-taxi-dataset lstm-autoencoder time-series-anomaly-detection
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Jun 28, 2022 - Jupyter Notebook
taxi-demand prediction model using deep learning
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Dec 3, 2018 - Jupyter Notebook
Visualize millions of yellow cab data in New York City from July 2015 - June 2016
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Mar 22, 2018 - JavaScript
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.…
python machine-learning deep-learning random-forest numpy linear-regression scikit-learn pandas python3 kaggle-competition xgboost matplotlib feature-engineering knn-regression xgboost-model nyc-taxi-dataset fastest-routes visualization-image google-colab remote-server
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Sep 21, 2018 - Jupyter Notebook
🚕 Predicting NYC Taxi Trip Duration with machine learning.
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May 8, 2021 - Jupyter Notebook
Scripts to the build a balanced panel of the 2013 NYC Taxi Data
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Apr 2, 2020 - R
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
machine-learning random-forest cross-validation prediction data-visualization data-preprocessing feature-engineering k-fold taxi-data random-forest-regressor nyc-taxi-dataset nyc-taxi ensemble-machine-learning nyc-taxi-fare
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Jun 13, 2019 - Jupyter Notebook
Given that a ton of open data available, an Analysis on the most relevant features that drive the house prices.
analysis nyc-taxi-dataset multiple-datasets nyc-housing-prize foursquare-dataset enigma-datathon nyc-crime-dataset
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May 26, 2018 - Jupyter Notebook
Python scripts to download, process, and analyze the New York City Taxi and Limousine Commission (TLC) Trip Record Data dataset
python nyc metadata data apache-spark etl s3 data-transformation pandas data-engineering data-extraction jupyterlab geopandas nyc-taxi-dataset lykmapipo apache-arrow joblib pyarrow fsspec
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Aug 22, 2024 - Jupyter Notebook
DevCon5 Community Meet up
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Sep 30, 2018 - 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.
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Feb 14, 2023 - Jupyter Notebook
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
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