🚦 NYC Collisions Analysis 

Overview:
Analyzed 238,000+ NYC motor vehicle collision records to uncover key patterns in road safety, contributing factors, and injury distribution. The project applied data cleaning, visualization, and exploratory analysis using Pandas, Matplotlib, and Seaborn.

Key Insights & Visualizations:

📊 Borough-wise Injuries: Bar chart revealed that Brooklyn and Queens had the highest number of injuries, highlighting them as key risk zones.

🗺️ Geographical Hotspots: Scatter plots of latitude/longitude showed concentrated clusters of collisions, with Manhattan showing dense patterns.

🧭 Injury Severity Mapping: Color-coded scatter plot emphasized areas with higher injury counts, useful for identifying dangerous intersections.

⚠️ Contributing Factors: Driver distraction, unsafe speeds, and failure to yield were among the top causes of accidents, as seen in the bar graph.

⏳ Temporal Trends: Time series analysis revealed seasonal peaks — summer months and weekends showed higher collision-related injuries.

🔗 Correlation Analysis: Heatmap showed strong relationships between Persons Injured and Motorists Injured, confirming consistent reporting patterns.

Technologies Used:

• Python (Pandas, Matplotlib, Seaborn)

• Data Cleaning & EDA

• Visualization & Trend Analysis
  
  

Impact:
This analysis highlights critical patterns in NYC traffic collisions, providing actionable insights for policy makers, traffic authorities, and urban planners to improve road safety.

For Source Code: