Project Summary

This project analyzes 12 months of historical ride data from Cyclistic to understand behavioral differences between casual riders and annual members. Using R for data cleaning and transformation and Tableau for visualization, I explored seasonal, hourly, geographic, and route-level patterns to identify factors that influence membership conversion.

What I did:

Cleaned and combined 12 CSV datasets (4M+ rows) using R (tidyverse/dplyr)
Engineered new variables (ride duration, month/day/hour, routes)
Built Tableau visualizations to compare usage patterns between rider types
Identified key behavioral segments and actionable conversion opportunities
Delivered strategic recommendations based on quantitative evidence

What I found:

Members ride consistently during weekday commuting hours
Casual riders are seasonal, weekend-focused, and leisure-oriented
Casual riders take longer rides and prefer round-trip routes
Key tourist stations show extremely high casual rider usage