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

A better dashboard for Uber Eats Drivers

EDA and data visualisation

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.

Uber Data Analysis and Visualization using Python

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

Power Bi Pro Studio

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

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.

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

Operations/demand analytics on 150K Uber ride bookings — hourly demand patterns, cancellation-rate diagnostics, and ops recommendations using Python (pandas, matplotlib)

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

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.