Health Trend Analysis in Urban Areas

Leveraging Python, R, and SQL, I developed a data-driven system to analyze health trends in major urban areas. The model took into account factors such as pollution levels, local medical facilities, population density, and lifestyle patterns.

I employed data visualization tools, primarily Matplotlib and Seaborn, to showcase health risk zones and areas with heightened medical concerns. This visualization helped city planners and health officials identify regions requiring immediate medical intervention or urban development changes.

By integrating insights from both the healthcare and urban development sectors, I was able to recommend urban planning strategies that not only improved general health outcomes but also significantly reduced hospital admission rates in targeted areas.

Moreover, the project emphasized the importance of preventive care, leading to the initiation of health awareness campaigns, especially in densely populated zones.

Outcome:

Identified and improved health risk zones in urban areas, reduced hospital admissions by 15% in targeted regions, and emphasized preventive care through awareness campaigns.