This project involves an in-depth analysis of Uber ride data to extract meaningful insights into ride patterns, user behavior, and usage trends. By exploring when, where, and why people use Uber, this project helps understand urban transportation patterns using real-world data.
The analysis addresses various questions using Python, Pandas, and data visualization libraries like Matplotlib and Seaborn. The project includes preprocessing, exploratory data analysis (EDA), visualizations, and insights based on 16 carefully selected questions.
- Understand and clean the Uber dataset for accurate analysis.
- Perform feature engineering (extracting day, hour, month, etc. from timestamps).
- Analyze ride behavior by time, location, purpose, and distance.
- Answer 16 practical business-driven questions using data.
- Present visual insights using clean and informative plots.
- Document the notebook clearly and upload it to GitHub for showcasing.
Uber_Project.ipynb– Main Colab Notebook containing all code, visualizations, and analysis.README.md– Overview and explanation of the project.
- In which category do people book the most Uber rides?
- For which purpose do people book Uber rides the most?
- At what time of day are Uber rides most frequently booked?
- In which months do people book Uber rides less frequently?
- On which days of the week do people book Uber rides the most?
- What is the typical distance (in miles) people travel through Uber?
- What is the average ride distance per ride purpose?
- Which location has the highest number of pickups?
- Which location has the highest number of drop-offs?
- What is the total number of rides for Business vs. Personal purposes?
- Which month had the highest total distance covered?
- How does ride frequency vary across weekdays vs. weekends?
- Which ride purpose has the highest total distance covered?
- Which routes are associated with the longest distances?
- Which trip purposes are associated with the longest distances?
- How do trip categories and purposes relate to each other?
- Business trips are the most frequent, highlighting Uber's role in professional travel.
- Early morning hours and weekdays see the most bookings.
- The most travelled routes and longest trips are often tied to business needs.
- Certain months and weekends show lower ride frequency, possibly indicating vacation/off-peak periods.
- Home and airport were among the top pickup and drop-off locations.
- Python (Pandas, NumPy)
- Colab
- Matplotlib & Seaborn for visualization
- Datetime for time-based feature engineering
- Clone this repository or download the notebook.
- Make sure you have the required Python libraries installed.
- Open the
Uber_Project.ipynbin Jupyter Notebook, VS Code or Colab. - Run the cells to explore the data and visualizations.
- Dataset used from Kaggle / Uber Data
- Project created as part of a data analysis learning journey
Feel free to reach out for any feedback and suggestions.
Harshdev Parmar
📧 Email: harsh.parmar03@gmail.com