Cyclistic Bike Sharing Project

Project 1: Cyclistic Bike Sharing

This project was a guided case study into how different bike share customers use bikes. There were two groups: members and casual riders. There are a significant number of casual riders in Chicago and Cyclistic bike-sharing wants to figure out how to attract those riders. This presentation covers the trends discovered in 12 months of ridership data and presents solutions for attracting casual riders to subscribe to a bike sharing membership. I used R to clean and analyze the data. Once the data was analyzed, I created visualizations for the presentation. 

GitHub R Code: https://github.com/campermatt589/R_Analysis_Projects/blob/main/Cyclistic_Analysis_in_R 

Data Source:

• Source of dataset: https://divvy-tripdata.s3.amazonaws.com/index.html 

Tools and Methods:

• Exploratory Data Analysis

• Data Cleaning

• R programming language

Lessons Learned:

• The amount of rides taken overwhelmingly favors membership riders. There is an uptick in rides taken by casual riders on the weekends. This could indicate that casual riders use the bikes for leisure. 

• Members use the bikes mostly on the weekdays. That could mean members are using bikes for commuting and leisure use during breaks. 

• Ride times for casual riders are more than 4 times longer than members. 

Recommendations: 

• There is higher usage on the weekends by casual riders, which is ideal for leisure usage. Account for the higher usage of bikes on Saturday and Sunday by casual riders. 

• Given the stark contrast in ride times between members and casual riders, Cyclistic should consider how ride times can affect the reasons why people use their bikes. 

• To attract casual riders, Cyclistic should appeal to them by advertising leisure and commute usage.