A machine learning project which predicts Uber trip data for different factors.
-
Updated
Apr 21, 2024 - Jupyter Notebook
A machine learning project which predicts Uber trip data for different factors.
In this Project i will do analysis on Uber data, will use some library which are mentioned below
The goal of this project is to track the expenses of Uber Rides and Uber Eats through data Engineering processes using technologies such as Apache Airflow, AWS Redshift and Power BI.
Uber and lyft data visualization, comparision and many analysis with python
To identify the root cause of cancellation and non-availability of cars addressing the Uber supply-demand gap.
This is the final data science project for USIT5609 MScIT Part II. Primarily made to learn Data Analytics, Machine Learning, and AI. To predict uber prices with external factors such as rain, temperature, time of day, day of the year, and more.
Explore your activity on Uber with R: How to analyze and visualize your personal data history. Find out how you consume the Uber App using a copy of your data.
Code for fetching, sampling, and analysis of NYC taxi data from TLC and Uber for 2009-2018
Uber Data Analysis and Visualization using Python
This app is integrated with UBER API. You can use uber features from your app.
Uber Traveling Time Analytics in DC Census Tract Zones
Exploratory and predictive data analysis with Uber's speeds dataset for London city.
EDA and data visualisation
Sources for some videos
This is an analysis for the supply demand gap faced by the Uber and taxi companies
Uber web interface crawler / scraper - Convert the trips table into a CSV file
Addressing some data science related issuesof a ride sharing app, Pathao
Add a description, image, and links to the uber-data topic page so that developers can more easily learn about it.
To associate your repository with the uber-data topic, visit your repo's landing page and select "manage topics."