The objective is to analyze the usage patterns of annual members and casual riders to gain insights that will inform a new marketing strategy. By answering key business questions, we aim to design effective strategies to convert casual riders into annual members and maximize Cyclistic's growth potential.
- Platform: Kaggle
- Language: R
The project utilizes the Cyclistic historical trip data for the previous 12 months. These datasets are publicly available and contain information on bike trips, customer types, and other relevant attributes. Please note that personally identifiable information has been removed to ensure data privacy.
data: Contains the Cyclistic trip data files.scripts: Includes R scripts for data preparation, analysis, and visualizations.reports: Contains the final report and key findings of the analysis.
To access the complete project and notebooks, please visit the Cyclistic Bike-Share Analysis) Kaggle project page.
This case study provides valuable insights into the differences between annual members and casual riders, allowing us to design a data-driven marketing strategy for Cyclistic. The analysis showcases the data analysis process, data cleaning, visualizations, and key recommendations using R programming language. I believe this case study will serve as a strong demonstration of my skills and expertise in data analysis.
Please refer to the Kaggle project page for detailed analysis steps, code, visualizations, and recommendations. Feel free to reach out with any questions or feedback. Happy analyzing!