Import public NYC taxi and for-hire vehicle (Uber, Lyft) trip data into a PostgreSQL or ClickHouse database

Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data

Analyzing 200 GB of NYC taxi dataset.

Design/Implement stream/batch architecture on NYC taxi data | #DE

Develop ML models predict taxi trip duration in NYC. Ranked : Top 6% | RMSLE : 0.377 (Kaggle) | #DS

Predict NYC taxi travel times (Kaggle competition)

A time-series, regression problem to find the number of pickups, given coordinates in NYC.

Final Project for the 'Machine Learning and Deep Learning' Course at AGH Doctoral School

taxi-demand prediction model using deep learning

Visualize millions of yellow cab data in New York City from July 2015 - June 2016

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.…

🚕 Predicting NYC Taxi Trip Duration with machine learning.

Scripts to the build a balanced panel of the 2013 NYC Taxi Data

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

Preprocessing TaxiNYC Data

Given that a ton of open data available, an Analysis on the most relevant features that drive the house prices.

DevCon5 Community Meet up

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.

Code for fetching, sampling, and analysis of NYC taxi data from TLC and Uber for 2009-2018

Winning AI Project for TUM.ai Makeathon October 2021 (scored 1st place), REACT-based application that predicts estimated demands for taxi rides in NYC's districts (time series analytics)