Uber dataset with 5,000 rides, including timestamps, city, distance, fare, driver/customer IDs, payment type, ratings, and ride status
Awesome — I built you a ready-to-run dashboard app.
Files
- ▶️ Streamlit app: uber_dashboard_streamlit.py
- 📂 Dataset (5,000 rides): uber_data.csv
How to run
- Put both files in the same folder.
- In a terminal:
pip install streamlit pandas numpy matplotlib streamlit run uber_dashboard_streamlit.py - If your CSV isn’t in the same folder, update the CSV path in the app’s sidebar.
What you get
- Sidebar filters: date range, city, status, payment type, distance & fare sliders.
- KPI cards: rides, revenue, avg fare, avg distance, completion rate, avg rating.
- Charts: rides & revenue over time, payment split, ratings distribution, rides by city, revenue by city, peak hours, cancellations by city.
- Driver leaderboard with trips, earnings, and avg rating.
Want me to enhance it with geo features (pickup/dropoff points + maps) or convert it to a Plotly Dash web app?
