Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data
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Updated
Jun 30, 2021
Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data
Develop ML models predict taxi trip duration in NYC. Ranked : Top 6% | RMSLE : 0.377 (Kaggle) | #DS
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
🗽🚕 Performance of data analysis in taxi trips in NYC and creation of a Random Forest Regressor in order to predict the duration of taxi trips.
Code for fetching, sampling, and analysis of NYC taxi data from TLC and Uber for 2009-2018
Analysis of human behaviour in NYC using taxi data
Examine relationship between NYC weather and taxi data from 2016
Visualization dashboard of NYC green taxi data using plotly-dash
Final project of Course Applied Data Science @nyu CUSP
NYC Taxi demand forecasting using machine learning and weather analytics. Includes end-to-end pipeline: data preprocessing, feature engineering, XGBoost forecasting, and Power BI dashboards.
End-to-End ETL pipeline for NYC Taxi data using Apache Airflow and PostgreSQL
Practice Programs of JAVABRAINS
Uncovering insights on NYC taxi fares and tipping behavior trends across boroughs by segmenting rides among various factors.
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