
Michelle Lai developed data analysis and visualization features for the Chameleon-company/MOP-Code repository, focusing on pedestrian crash risk assessment. She designed and documented preprocessing workflows and exploratory data analysis using Python, Pandas, and Seaborn, enabling reproducible analytics and supporting safety planning. Michelle built a Streamlit dashboard that visualizes risk factors, spatial distributions, and correlation heatmaps, providing stakeholders with actionable insights. Her technical writing improved onboarding and collaboration by clarifying project pipelines and repository structure. The work emphasized transparency, maintainability, and data-driven decision making, demonstrating depth in both engineering and documentation practices over the course of two months of focused development.

September 2025: Delivered two main features in Chameleon-company/MOP-Code with a strong emphasis on documentation, onboarding, and data-driven analytics. No major production bugs identified this month; focus was on improving clarity, collaboration, and decision-support capabilities that increase developer velocity and stakeholder visibility.
September 2025: Delivered two main features in Chameleon-company/MOP-Code with a strong emphasis on documentation, onboarding, and data-driven analytics. No major production bugs identified this month; focus was on improving clarity, collaboration, and decision-support capabilities that increase developer velocity and stakeholder visibility.
August 2025 monthly summary for Chameleon-company/MOP-Code: Delivered documentation and visualization assets for the pedestrian crash risk analysis project, emphasizing preprocessing workflow, exploratory data analysis (EDA), and spatial/relational insights to support risk assessment and safety planning. This work improves reproducibility, stakeholder communication, and data-driven decision making.
August 2025 monthly summary for Chameleon-company/MOP-Code: Delivered documentation and visualization assets for the pedestrian crash risk analysis project, emphasizing preprocessing workflow, exploratory data analysis (EDA), and spatial/relational insights to support risk assessment and safety planning. This work improves reproducibility, stakeholder communication, and data-driven decision making.
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