Cyclistic Bike-Share Analysis

Tools used in this project
Cyclistic Bike-Share Analysis

About this project


Welcome to the Cyclistic bike-share analysis case study! In this case study, I am assuming myself as a junior data analyst for this company. I have been asked to analyze the data from the previous financial year(2019-2020). This case study will go through the six steps of data analysis: Ask, Prepare, Process, analyze, Share, and Act.


Design marketing strategies aimed at converting casual riders into annual members.


The following questions will guide the future marketing program for Cyclistic: -

  1. How do annual members and casual riders use Cyclistic bikes differently?

  2. Why would casual riders buy Cyclistic annual memberships?

  3. How can Cyclistic use digital media to influence casual riders to become members?

I have been assigned the first question, and this case study will explore the differences between casual riders and annual members, with the ultimate goal of suggesting plans to convert casual riders into members. Let's get into it.


I will use Cyclistic’s historical trip data to analyze and identify trends. The data has been made available by Motivate International Inc. under this License:- https://www.divvybikes.com/data-license-agreement.

The data can be downloaded here:- https://divvy-tripdata.s3.amazonaws.com/index.html I verified the data's integrity by downloading the datasets from this website, compiling and comparing the datasets into one, and comparing the entries with the datasets from Kaggle. The results were the same time (date and time), end time (date and time), type of bike (Classic Bike, Docked Bike, or Electric Bike), and whether the customer was a member or a casual (non-member). This will help answer the business question, as we can see what differentiates the casual customers from the members.



Now that the data has been loaded into R, we need to clean and process the data. This includes checking the data for any errors or inconsistencies so I will be working with R, as it is a powerful data analysis tool and is easy to document the cleaning process.

undefinedundefinedundefinedundefinedundefinedundefined## Check the structure of the data


The data has been cleaned and is ready to analyze. Since the shareholders are interested in looking at customer behavior, we will focus our attention on the "ride length" metric. We will perform various calculations to obtain the mean, median, maximum value (longest ride), and minimum value (shortest ride).


Now we have our data in analyzed form, we need to be able to share it with the shareholders. A good way to do this is by using data visualizations. I will be presenting my findings in the form of bar graphs. The graphs show the rider's behavior based on the data.




Based on the above analysis, and looking at the graphs:

  1. Casual riders rent bikes more on the weekends (Sat, Sun) during summer months (Jun, Jul, Aug, Sep) other than winter months. Therefore, I would recommend doing a marketing campaign that targets casual riders on the weekend(Sat, Sun) during the summer months (Jun, Jul, Aug, Sep).

  2. Casual riders have more average duration time than member riders. As expected more casual riders ride bikes during winter. So I would suggest to tells the benefits of membership provided by a company to the casual riders.

To Access Complete Notebook:- Cyclistic Notebook

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