This project performs Exploratory Data Analysis (EDA) on historical Uber ride data to uncover ride trends, demand patterns, and time-based insights using Python-based data tools.
- Understand user ride behaviors
- Analyze patterns in time, location, and frequency
- Identify peak demand hours and days
- Visualize insights for better decision-making
- The dataset includes Uber pickups with fields like:
- Date/Time
- Latitude/Longitude
- Base Codes
- 📍 Source: Kaggle – Uber Pickups in New York City
- 📈 Trip frequency across hours, days, and months
- 🕒 Identification of peak demand times
- 📅 Day-of-week ride distribution
- 🌇 Pickup concentration across NYC boroughs
- 📌 Trends grouped by Uber base codes
- Hourly and daily trip distribution bar charts
- Monthly heatmaps for trend tracking
- Location-based scatter plots for pickups
- Aggregated statistics by date and base
- Python for data analysis
- Pandas and NumPy for data processing
- Matplotlib, Seaborn, and Plotly for visualizations
- Jupyter Notebook for interactive analysis
data/– raw dataset filesnotebooks/– main analysis notebookvisuals/– saved charts and plotsREADME.md– project documentation
- Download or clone the repository
- Open the analysis notebook in Jupyter
- Follow the visual and tabular outputs to explore ride patterns
Understanding transportation usage patterns helps:
- Improve rideshare logistics
- Inform traffic and urban planning
- Offer better customer experiences via data
Abinesh M
🔗 GitHub
📧 your.email : m.abinesh555@gmail.com
This project is licensed under the MIT License