This project analyzes BayWheels trip data to gain insights into user behavior, trip patterns, and overall system performance. The dataset includes details about individual trips, user types, and station usage trends.
The dataset consists of structured CSV files containing:
- Trip ID: Unique identifier for each trip
- Start Time & End Time: Timestamp of trip initiation and completion
- Start & End Station: Locations where the trip started and ended
- User Type: Subscriber or customer classification
- Bike ID: Unique identifier for each bike
- Identify peak usage hours and popular stations
- Analyze trip duration distribution
- Compare subscriber vs. customer usage patterns
- Identify trends in bike demand
- Python: Data processing and analysis
- Pandas: Data manipulation
- Matplotlib & Seaborn: Data visualization
- Jupyter Notebook: Interactive analysis
- Clone this repository:
git clone https://github.com/yourusername/baywheels-trip-analysis.git
- Navigate to the project directory:
cd baywheels-trip-analysis - Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook:
jupyter notebook
- Load the dataset in Jupyter Notebook.
- Run the analysis scripts to visualize trends and extract insights.
- Modify or extend the scripts for custom analysis.
- Identified high-traffic stations.
- Observed peak usage during morning and evening hours.
- Noted a higher proportion of subscribers compared to customers.
- Found seasonal variations in bike usage.