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.

A predictive model to help Uber drivers make more money

Shiny App to play with uber data

Analysis of Uber Data from NYC Open Data website

Uber web interface crawler / scraper - Convert the trips table into a CSV file

Exploratory and predictive data analysis with Uber's speeds dataset for London city.

Machine Learning Key Projects

EDA and data visualisation

A better dashboard for Uber Eats Drivers

Uber Data Analysis and Visualization using Python

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.

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

Addressing some data science related issuesof a ride sharing app, Pathao

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.

Uber and lyft data visualization, comparision and many analysis with python

A machine learning project which predicts Uber trip data for different factors.

This is an analysis for the supply demand gap faced by the Uber and taxi companies

This app is integrated with UBER API. You can use uber features from your app.

This project analyzes Uber trip data using Power BI to identify trends and patterns in ride demand. Data cleaning and transformation were performed using Power Query, and an interactive dashboard was developed to visualize key insights such as peak hours and location-based trip distribution.