URBAN TRAFFIC FLOW OPTIMIZATION

This project built under DataVision event in the technical fest , Avishkar at MNNIT , It identifies traffic congestion hotspots, peak hours, and optimized traffic signal timings and contibutes to improve overall traffic efficiency and reducing commuter stress

Tools & Libraries Used:

  • NumPy: For numerical computing and array manipulation.
  • Pandas: For data manipulation and analysis.
  • Seaborn & Matplotlib: For data visualization.
  • Scikit-learn: For building machine learning models.
  • SQL: Writing queries, data extraction, and database management.
  • Power BI: For creating impactful and interactive data visualizations.