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Building relational database and analyzing Divvy's ridership to understand its rider behaviors and identify external factors (e.g. weather conditions, days of the week) that impact their ridership.

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minhdvo1703/Divvy-Bike-Data-Engineering-Project

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Divvy - Bike-Sharing System in Chicago Analysis

MSCA 31012 - Data Engineering Platforms - Autumn 2022

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Executive Summary

Divvy is a bike-sharing system in Chicago, owned by the Chicago Department of Transportation. It was launched in 2013 and purchased by Lyft in 2019. Divvy is considered one of the primary bike-share system in Chicago,  with over 400,000 new riders and 5.5 million rides taken in 2021. Divvy has two primary group users, which are annual membership riders and casual riders. One of Divvy’s goals is to increase the number of annual membership riders. This project will focus on in-depth analysis of Divvy’s rider behavior and how external factors impact its ridership.

Business Case

Through this project, Divvy wants to understand more about their both group riders and which factors that may impact their ridership so they can improve their bike-sharing system and bike allocation, as well as develop marketing strategies to the bike-sharing system and attract more annual membership riders.
Particularly, Divvy leadership want to understand the ridership and bicycle allocation by stations, like which stations tend to have the most riders and bikes or bike docks. Weather conditions such as temperature, precipitation, snow, or windspeed can be key factors that influence Divvy ridership. In terms of riders, we want to understand more about the rider regarding user groups and demographic (population, age). We will also analyze their behaviors based on their average using time and average traveling distance, day of the week or time of the day has the highest number of riders, etc.

Datasets used in the project

Database & tools

MySQL, Google Cloud Platforms, Excel, Python, R, Tableau

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Building relational database and analyzing Divvy's ridership to understand its rider behaviors and identify external factors (e.g. weather conditions, days of the week) that impact their ridership.

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