NEVIN MCDONALD

Housing First To End Homelessness; Employed linear regression modeling techniques in the context of homelessness alleviation strategies, particularly focusing on "Housing First" initiatives.

• Integrated Python programming to develop predictive models aimed at estimating housing prices.
• Key variables included the year of construction, renovation, average bedroom count, and overall housing quality.
• Facilitated data-driven decision-making to support efforts aimed at ending homelessness.
• Provided valuable insights into housing affordability and accessibility.

Contractual Work in the City of Charlotte

A question into the costs, allocations, and distribution of contractual work within the city of Charlotte

• Utilized various data modeling techniques, including regression analysis and time series analysis, to dissect budget allocation trends.
• Implemented scatter plots, trend lines, and heat maps as visualizations to illustrate correlations, spatial patterns, and temporal trends in budget data.
• Integrated interactive dashboards and storytelling techniques to present comprehensive insights and recommendations to stakeholders effectively.
• Employed various data modeling techniques beyond clustering, such as regression analysis, to further dissect budget allocation trends.
• Utilized regression analysis to identify relationships between different variables within the budget data, such as the impact of economic indicators on spending patterns.