Cyclistic Case Study: Rider Behavior Analysis (2022)

Overview

This case study analyzes how annual members and casual riders use Cyclistic bike‑share services differently. Using 5.6M+ rides from 2022 and Python, the analysis explores ride length, day of week, bike type, and seasonality to support membership growth strategies.

Tools Used

Python (Pandas, NumPy, Matplotlib, Seaborn)
Jupyter Notebook
Google Sheets
Data visualization techniques
Data cleaning and transformation
Exploratory data analysis (EDA)

Process Summary

Data Collection: Combined 12 months of Cyclistic ride data (5.6M+ rows).
Data Cleaning: Removed nulls, fixed data types, calculated ride length, filtered invalid rides.
Feature Engineering: Extracted day of week, hour of day, ride type, and user type.
Exploratory Analysis: Identified behavioral patterns across members vs casual riders.
Visualization: Created charts to highlight trends in usage, duration, and bike type.
Insights & Recommendations: Developed actionable strategies to increase membership conversions.