This project is a data analysis of the bike-share company, Cyclistic, that operates in the Chicago area. The company has a membership model that allows riders to sign up for annual membership, casual pass or single ride depending on their frequency of bike usage.
The aim of this project is to help Cyclistic executives understand how casual riders and annual members use Cyclistic bikes differently. By understanding these differences, the company can use that insights to convert casual riders into annual members.
The analysis includes exploratory data analysis, data cleaning, feature engineering. All the code is written in R
To get started with this project, you will need to clone or download the project repository onto your local machine. The following libraries are required to run the project:
tidyverse lubridate janitor ggplot2 knitr caret dplyr
Once you have downloaded the required libraries, you can run the analysis in the Cyclistic-Bike-Share-Analysis.Rmd file.
The data for this project is provided by Cyclistic and it includes the company's trip data 2021.
Cyclistic-Bike-Share_data
The analysis is divided into three main sections: exploratory data analysis, feature engineering.
In the exploratory data analysis section, we investigate the patterns and trends in the data by answering questions such as: What is the average trip duration? When are the peak hours for bike usage? What are the most popular bike stations?
In the feature engineering section, we create new features based on the insights gained from the exploratory data analysis. For example, we create a new feature that indicates the day of the week and the hour of the day.
The results of the analysis are presented in the Cyclistic-Bike-Share-Analysis.html file. The file includes all the code, visualizations, and explanations of the insights gained from the analysis.
In conclusion, this project provides valuable insights into how casual riders and annual members use Cyclistic bikes differently. The analysis shows that annual members use the bikes more frequently and for shorter durations than casual riders. Based on these insights, Cyclistic can create targeted marketing campaigns to convert casual riders into annual members.
This project was carried out as part of the Google Data Analytics Professional Certificate course on Coursera. Special thanks to the instructors and mentors for their guidance and support throughout the course.