Welcome to the NYC Subway Ridership Data Analysis project, where we take a deep dive into the ups, downs, and all-arounds of New York City's iconic subway system. This project combines data analysis, visualization, and storytelling to uncover the trends that keep NYC moving—or not moving (thanks, COVID-19 🦠). By integrating various datasets, it provides insights into borough-level recovery, subway line popularity, and shifting commuter preferences, with an eye toward predicting future trends.
- Analyze subway ridership trends pre- and post-COVID.
- Understand borough-wise and line-specific recovery patterns.
- Explore alternative transit modes (like CitiBike and Uber).
- Predict future ridership trends using data-driven models.
- Python: Data cleaning, analysis, and visualization.
- Tableau: Interactive dashboards for storytelling.
- Pandas, Matplotlib, Plotly: For deep dives into the data.
Data: Includes raw and processed datasets to support full reproducibility. Notebooks: Step-by-step EDA and analysis workflows. Dashboards: Interactive Tableau dashboards that bring the data to life. Scripts: Python scripts for data cleaning, merging, and predictive modeling. Reports: Final deliverables summarizing findings and actionable insights.
Subway ridership in NYC hit rock bottom in April 2020, with only 8.3% of pre-pandemic levels. Recovery is ongoing, with 2024 ridership still below 70% of 2019 figures.
Manhattan remains the hub for ridership, but Brooklyn and Queens show stronger recovery rates, indicating possible shifts in commuting patterns.
Popular lines like the 1, 2, and 3 saw faster recovery compared to underutilized lines like the G.
CitiBike and Uber usage surged during the pandemic, reflecting a preference for private and flexible travel options.