# 🚕 CityPulse – Smart Urban Ride Demand Forecasting
CityPulse is an interactive AI-powered dashboard built with Streamlit that predicts high-demand taxi ride zones in New York City. It enables users to explore urban transportation trends through precomputed machine learning predictions, dynamic filters, and intuitive visualizations.
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📅 Day & Hour Filtering
Analyze ride demand by selecting specific weekdays and time ranges. -
🚕 Trip Distance Control
Use an interactive slider to focus on short, medium, or long-distance trips. -
📊 Dynamic KPIs & Visualizations
View key performance indicators, demand trends, and insightful charts. -
🗺️ Simulated Ride Map
Explore predicted high-demand zones using mock geographic coordinates.
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🚀 Live App:
https://citypulse-jejz2fvmxdm6c5tzej5apd.streamlit.app/ -
📁 GitHub Repository:
https://github.com/grave021999/citypulse
| Category | Tools / Technologies |
|---|---|
| Frontend | Streamlit |
| Data Processing | Pandas, NumPy |
| Machine Learning | Precomputed Predictions |
| Deployment | Streamlit Community Cloud |
| Version Control | Git, GitHub |
git clone https://github.com/grave021999/citypulse.git
cd citypulsepython -m venv venvActivate it:
- Windows:
venv\Scripts\activate- Mac/Linux:
source venv/bin/activatepip install -r requirements.txtstreamlit run app/dashboard.py- 📍 Replace simulated coordinates with real GPS data
- ⚡ Integrate live ML model APIs for real-time predictions
- 🌍 Add advanced geospatial heatmaps for city-wide insights
CityPulse can be used by:
- Urban planners to understand transportation demand
- Ride-sharing platforms for demand optimization
- Data enthusiasts exploring real-world ML applications
- Recruiters evaluating applied data engineering + analytics skills
Mohammad Atif
- 🔗 LinkedIn: https://www.linkedin.com/in/atif0201/
If you like this project:
- ⭐ Star the repository
- 🍴 Fork it and contribute
- 🧠 Share feedback or ideas