https://github.com/alrza2003/google-data-analysis-case-study-cyclistic
This project analyzes Cyclistic’s trip data to identify patterns in bike usage between casual riders and annual members. The findings help optimize marketing strategies and membership conversions.
https://github.com/alrza2003/google-data-analysis-case-study-cyclistic
business-task cyclistic-bike-share-analysis-case-study data-analysis data-science data-visualization google-data-analytics google-data-analytics-capstone-project google-data-analytics-professional jupyter-notebook python rmarkdown tableau
Last synced: about 1 month ago
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This project analyzes Cyclistic’s trip data to identify patterns in bike usage between casual riders and annual members. The findings help optimize marketing strategies and membership conversions.
- Host: GitHub
- URL: https://github.com/alrza2003/google-data-analysis-case-study-cyclistic
- Owner: AlrzA2003
- License: mit
- Created: 2025-05-05T06:23:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-06T15:34:45.000Z (about 1 year ago)
- Last Synced: 2025-07-31T10:17:53.062Z (11 months ago)
- Topics: business-task, cyclistic-bike-share-analysis-case-study, data-analysis, data-science, data-visualization, google-data-analytics, google-data-analytics-capstone-project, google-data-analytics-professional, jupyter-notebook, python, rmarkdown, tableau
- Language: Jupyter Notebook
- Homepage:
- Size: 3.15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cyclistic Trip Analysis
Cyclistic Trip Analysis is a data-driven project designed to examine bike usage patterns among **casual riders** and **annual members**. Using historical trip data, this project uncovers key behavioral insights that help inform **membership conversion strategies** and operational decisions.
## Table of Contents
- [Project Overview](#project-overview)
- [Dataset](#dataset)
- [Tools and Technologies](#tools-and-technologies)
- [Data Processing](#data-processing)
- [Key Findings](#key-findings)
- [Results Sharing](#results-sharing)
- [Business Implications](#business-implications)
- [License](#license)
## Project Overview
This project analyzes **Cyclistic’s bike trip data** to identify differences in riding patterns between casual users and annual members. The goal is to extract insights that inform **marketing strategies**, **service optimizations**, and **customer engagement efforts**. By leveraging data science techniques, we provide actionable recommendations to enhance **membership conversion** and improve service efficiency.
## Dataset
The dataset used in this analysis is sourced from **Motivate International Inc.**, a credible first-party provider. The trip data spans **March 2024 to February 2025** and includes details on ride duration, user type, timestamps, and geographic coordinates. The raw dataset was obtained from [Divvy Trip Data](https://divvy-tripdata.s3.amazonaws.com/index.html), and further processed for consistency and integrity.
Additional geographical data (**ward1998.zip**) containing coordinates for Chicago has been integrated for spatial analysis within `annual_trips_process.ipynb`.
The **complete dataset** used for this analysis is available at the following link:
[Download Full Dataset](https://1drv.ms/u/c/32ad82fef2c1dc75/EdGTQ3_iwKVBhXW6pcvI3kEBmfTw_ezzONdN95BlTMwvRQ?e=mTaDw4).
## Tools and Technologies
This project is built using the following tools:
- **Python** (Pandas, NumPy, Matplotlib, Seaborn)
- **Jupyter Notebook** (`annual_trips_process.ipynb`, `annual_trips_analyze.ipynb`, `annual_trips_share.ipynb`)
- **R Markdown** (`cyclistics-trips.Rmd`) – Contains the full **written analysis** in report format
- **CSS** (for styling reports, `styles.css`)
- **PowerPoint** (`Annual_Trips_Insights.pptx`) – Offers **a more visual representation** of key findings
- **Tableau Public Dashboard** ([Cyclistic Trip Analysis](https://public.tableau.com/views/Book2_17462764518790/HowdoannualmembersandcasualridersuseCyclisticbikesdifferently?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link))
## Data Processing
### **Preprocessing (`annual_trips_process.ipynb`)**
- Merging raw data into a **single dataset** for improved accessibility
- Removing **duplicates** (211 records) and filtering out **erroneous data**
- Creating **additional fields** for analysis:
- **ride_length** (duration of each trip)
- **day_of_week** (categorizing ride start days)
- **length_dif_secs** (time difference in seconds)
### **Analysis (`annual_trips_analyze.ipynb`)**
- **Descriptive statistics** on ride duration and frequency
- **Patterns in peak riding times** (weekday vs. weekend comparisons)
- **Differences in bike type preferences**
### **Sharing & Visualization (`annual_trips_share.ipynb`)**
- Generating plots for ride distribution trends
- **Seasonality analysis** (Monthly usage variations)
## Key Findings
### **Ride Length Trends**
Casual riders have **longer trips on average** compared to annual members, suggesting they primarily use Cyclistic for leisure rather than commuting.
### **Weekday vs. Weekend Usage**
- Casual riders **peak on Thursdays–Saturdays**, with fewer trips early in the week.
- Annual members **have fluctuations in ridership**, peaking on Tuesdays and declining on Fridays & Saturdays.
### **Seasonal Variations**
Casual ridership **spikes in warm months (May–September)**, whereas annual members maintain **steady engagement** year-round.
## Results Sharing
The insights generated have been shared through multiple platforms:
- **PowerPoint Presentation** (`Annual_Trips_Insights.pptx`) – Provides a **clear, visual representation** of key findings with graphs and charts.
- **R Markdown Report** (`cyclistics-trips.Rmd`) – Contains the full **detailed written analysis**, explaining methodology and findings in-depth.
- **Tableau Public Dashboard** ([Interactive Insights](https://public.tableau.com/views/Book2_17462764518790/HowdoannualmembersandcasualridersuseCyclisticbikesdifferently?:language=en-GB&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link)) – Allows for **interactive exploration** of trip patterns.
## Business Implications
The findings present several strategic recommendations:
- **Membership Promotions:** Incentives during casual riders' peak demand periods (Thursdays–Saturdays) to encourage annual subscriptions.
- **Seasonal Marketing Campaigns:** Emphasizing membership benefits during summer peaks to increase engagement.
- **Flexible Membership Models:** Trial memberships for casual riders with long trip durations.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.