{"id":28023792,"url":"https://github.com/alrza2003/google-data-analysis-case-study-cyclistic","last_synced_at":"2026-05-09T16:33:18.053Z","repository":{"id":291795106,"uuid":"977893203","full_name":"AlrzA2003/Google-Data-Analysis-Case-Study-Cyclistic","owner":"AlrzA2003","description":"This project analyzes Cyclistic’s trip data to identify patterns in bike usage between casual riders and annual members. 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Using historical trip data, this project uncovers key behavioral insights that help inform **membership conversion strategies** and operational decisions.  \n\n## Table of Contents  \n- [Project Overview](#project-overview)  \n- [Dataset](#dataset)  \n- [Tools and Technologies](#tools-and-technologies)  \n- [Data Processing](#data-processing)  \n- [Key Findings](#key-findings)  \n- [Results Sharing](#results-sharing)  \n- [Business Implications](#business-implications)  \n- [License](#license)  \n\n## Project Overview  \n\nThis 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.  \n\n## Dataset  \n\nThe 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.  \n\nAdditional geographical data (**ward1998.zip**) containing coordinates for Chicago has been integrated for spatial analysis within `annual_trips_process.ipynb`.  \n\nThe **complete dataset** used for this analysis is available at the following link:  \n[Download Full Dataset](https://1drv.ms/u/c/32ad82fef2c1dc75/EdGTQ3_iwKVBhXW6pcvI3kEBmfTw_ezzONdN95BlTMwvRQ?e=mTaDw4).  \n\n## Tools and Technologies  \n\nThis project is built using the following tools:  \n- **Python** (Pandas, NumPy, Matplotlib, Seaborn)  \n- **Jupyter Notebook** (`annual_trips_process.ipynb`, `annual_trips_analyze.ipynb`, `annual_trips_share.ipynb`)  \n- **R Markdown** (`cyclistics-trips.Rmd`) – Contains the full **written analysis** in report format  \n- **CSS** (for styling reports, `styles.css`)  \n- **PowerPoint** (`Annual_Trips_Insights.pptx`) – Offers **a more visual representation** of key findings  \n- **Tableau Public Dashboard** ([Cyclistic Trip Analysis](https://public.tableau.com/views/Book2_17462764518790/HowdoannualmembersandcasualridersuseCyclisticbikesdifferently?:language=en-GB\u0026:sid=\u0026:redirect=auth\u0026:display_count=n\u0026:origin=viz_share_link))  \n\n## Data Processing  \n\n### **Preprocessing (`annual_trips_process.ipynb`)**  \n- Merging raw data into a **single dataset** for improved accessibility  \n- Removing **duplicates** (211 records) and filtering out **erroneous data**  \n- Creating **additional fields** for analysis:  \n  - **ride_length** (duration of each trip)  \n  - **day_of_week** (categorizing ride start days)  \n  - **length_dif_secs** (time difference in seconds)  \n\n### **Analysis (`annual_trips_analyze.ipynb`)**  \n- **Descriptive statistics** on ride duration and frequency  \n- **Patterns in peak riding times** (weekday vs. weekend comparisons)  \n- **Differences in bike type preferences**   \n\n### **Sharing \u0026 Visualization (`annual_trips_share.ipynb`)**  \n- Generating plots for ride distribution trends\n- **Seasonality analysis** (Monthly usage variations)    \n\n## Key Findings  \n\n### **Ride Length Trends**  \nCasual riders have **longer trips on average** compared to annual members, suggesting they primarily use Cyclistic for leisure rather than commuting.  \n\n### **Weekday vs. Weekend Usage**  \n- Casual riders **peak on Thursdays–Saturdays**, with fewer trips early in the week.  \n- Annual members **have fluctuations in ridership**, peaking on Tuesdays and declining on Fridays \u0026 Saturdays.  \n\n### **Seasonal Variations**  \nCasual ridership **spikes in warm months (May–September)**, whereas annual members maintain **steady engagement** year-round.  \n\n## Results Sharing  \n\nThe insights generated have been shared through multiple platforms:  \n- **PowerPoint Presentation** (`Annual_Trips_Insights.pptx`) – Provides a **clear, visual representation** of key findings with graphs and charts.  \n- **R Markdown Report** (`cyclistics-trips.Rmd`) – Contains the full **detailed written analysis**, explaining methodology and findings in-depth.  \n- **Tableau Public Dashboard** ([Interactive Insights](https://public.tableau.com/views/Book2_17462764518790/HowdoannualmembersandcasualridersuseCyclisticbikesdifferently?:language=en-GB\u0026:sid=\u0026:redirect=auth\u0026:display_count=n\u0026:origin=viz_share_link)) – Allows for **interactive exploration** of trip patterns.  \n\n## Business Implications  \n\nThe findings present several strategic recommendations:  \n- **Membership Promotions:** Incentives during casual riders' peak demand periods (Thursdays–Saturdays) to encourage annual subscriptions.  \n- **Seasonal Marketing Campaigns:** Emphasizing membership benefits during summer peaks to increase engagement.  \n- **Flexible Membership Models:** Trial memberships for casual riders with long trip durations.  \n\n## License  \n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falrza2003%2Fgoogle-data-analysis-case-study-cyclistic","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falrza2003%2Fgoogle-data-analysis-case-study-cyclistic","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falrza2003%2Fgoogle-data-analysis-case-study-cyclistic/lists"}