NYC Motor Vehicle Collision Analysis

Problem

ML analysis predicting high-collision regions across NYC boroughs. This project addressed the challenge of ml, data science, researchin the context of ml engineer work.

Constraints

• • Timeframe: Aug–Dec 2019
• • Role: ML Engineer
• • Stack: Python, Scikit-learn, Naive Bayes, Seaborn, Tableau

Process

The development process involved iterative design, implementation, and testing phases. Key focus areas included ml, data science, research considerations and ensuring measurable outcomes.

Results

• 78.17% prediction accuracy
• Interactive dashboard for stakeholders
• Data-driven safety recommendations

Lessons Learned

This project reinforced the importance of ml, data science, researchbest practices and the value of iterative development approaches.